# omni-ai-mcp - Doramagic AI Context Pack > Positioning: a pre-install experience and judgment asset. It helps the host AI get off to a good start, but it does not mean the project has already been installed, run, or validated. ## Sufficiency Principle - **Sufficiency over compression**: The AI Context Pack should be sufficient for the host AI to understand the project's value, capability boundaries, entrypoints, risks, and evidence sources before starting work; it may be layered, but it does not aim for the shortest possible summary. - **Compression policy**: Compress only noise and duplication, never context that affects judgment or the quality of the work. ## How the Host AI Should Use This You are reading the AI Context Pack that Doramagic compiled for omni-ai-mcp. Treat it as pre-work context: help the user understand who it fits, what it can do, how to start, what must be verified after install, and where the risks are. Do not claim that you have already installed, run, or executed the target project. ## Claim Consumption Rules - **Fact source**: Repo Evidence + Claim/Evidence Graph; the Human Wiki only supplies salience, terminology, and narrative structure. - **Minimum status for a fact**: `supported` - `supported`: May be used as a project fact, but the answer must cite the claim_id and evidence path. - `weak`: Usable only as a low-confidence lead; the user must be asked to keep verifying. - `inferred`: Usable only for risk notes or open questions; must not be packaged as a project fact. - `unverified`: Must not be used as fact; state clearly that evidence is insufficient. - `contradicted`: Must show the conflicting sources and must not force a single version on the user's behalf. ## Who It Fits Best - **Developers already using host AIs such as Claude/Codex/Cursor/Gemini**: The README or plugin config mentions multiple host AIs. Evidence: `README.md` Claim: `clm_0003` supported 0.86 ## What It Can Do - **Multi-Host Install and Distribution** (Verify after install): The project contains plugin or marketplace configuration, indicating it targets install and distribution across one or more AI hosts. Evidence: `.claude-plugin/plugin.json` Claim: `clm_0001` supported 0.86 - **Command-Line Startup or Install Flow** (Verify after install): The project documentation contains runnable commands; real use requires running them in a local or host environment. Evidence: `README.md` Claim: `clm_0002` supported 0.86 ## How to Start - `pip install omni-ai-mcp` Evidence: `README.md` Claim: `clm_0004` supported 0.86 - `git clone https://github.com/marmyx77/omni-ai-mcp.git` Evidence: `README.md` Claim: `clm_0005` supported 0.86 - `pip install 'mcp[cli]>=1.0.0' 'google-genai>=2.0.0' pydantic defusedxml filelock` Evidence: `README.md` Claim: `clm_0006` supported 0.86 - `claude mcp add omni-ai-mcp --scope user \` Evidence: `README.md` Claim: `clm_0007` supported 0.86 ## Continue-or-Stop Decision Card - **Current recommendation**: Needs admin / security approval - **Why**: Continuing may involve secrets, accounts, external services, or sensitive context; get admin or security approval first. ### 30-Second Read - **What to do now**: Needs admin / security approval - **Minimum safe next step**: Run Prompt Preview first; if credentials or an enterprise environment are involved, get approval before trialing - **Do not trust yet**: Role quality and task fit cannot be trusted directly. - **Continuing will touch**: Role selection bias, Command execution, Host AI configuration ### What You Can Trust Now - **Target-audience signal: Developers already using host AIs such as Claude/Codex/Cursor/Gemini** (supported): Backed by a supported claim or project evidence, but that still is not the same as real install results. Evidence: `README.md` Claim: `clm_0003` supported 0.86 - **Capability exists: Multi-Host Install and Distribution** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `.claude-plugin/plugin.json` Claim: `clm_0001` supported 0.86 - **Capability exists: Command-Line Startup or Install Flow** (supported): You can trust that the project contains signals of this capability; whether it fits your specific task still needs trial or after-install verification. Evidence: `README.md` Claim: `clm_0002` supported 0.86 - **There are Quick Start / install-command signals** (supported): You can trust that the docs mention a startup or install entrypoint; do not run it directly in your primary environment because of that. Evidence: `README.md` Claim: `clm_0004` supported 0.86 ### What You Cannot Trust Yet - **Role quality and task fit cannot be trusted directly.** (unverified): A role library proves there are many roles; it does not prove each one fits your specific task or that a role produces high-quality results. - **Do not treat role copy as real execution capability.** (unverified): Before install you can only judge whether the role description and task profile match; you cannot prove it can complete the task inside the host AI. - **Real output quality cannot be trusted before install.** (unverified): Prompt Preview can only show how it guides you; it cannot prove result quality in the real project. - **Host AI version compatibility cannot be trusted before install.** (unverified): Host loading rules and version differences across Claude, Cursor, Codex, Gemini, and others must be verified in a real environment. - **That it will not pollute your existing host AI's behavior cannot be trusted directly.** (inferred): Skill, plugin, and AGENTS/CLAUDE/GEMINI instructions may change the host AI's default behavior. Evidence: `.claude-plugin/plugin.json`, `CLAUDE.md` - **Safe rollback cannot be assumed by default.** (unverified): Unless the project clearly provides uninstall and recovery instructions, verify in an isolated environment first. - **After a real install, is it compatible with the user's current host AI version?** (unverified): Compatibility can only be verified in the actual host environment. Evidence: `.claude-plugin/plugin.json` - **Does the project's output quality meet the user's specific task?** (unverified): The pre-install preview can only show flow and boundaries; it cannot replace real evaluation. ### What Continuing Will Touch - **Role selection bias**: The user's judgment about which expert role should handle the task. Why: Picking the wrong role makes the AI answer from the wrong expert perspective, wasting time or misleading decisions. - **Command execution**: Package managers, network downloads, the local plugin directory, project config, or the user's home directory. Why: Running the very first command can already change your environment; decide whether it is worth running first. Evidence: `README.md` - **Host AI configuration**: The plugin, Skill, or rule-loading config of hosts like Claude/Codex/Cursor/Gemini/OpenCode. Why: Host configuration changes how the AI works afterward and may conflict with the user's existing rules. Evidence: `.claude-plugin/plugin.json`, `CLAUDE.md` - **Local environment or project files**: Install results, plugin caches, project config, or local dependency directories. Why: The write scope and rollback path cannot be proven before install and need isolated verification. Evidence: `.claude-plugin/plugin.json`, `README.md` - **Environment variables / API keys**: Project entry docs explicitly showing API key, token, secret, or account credential configuration. Why: If a real install needs credentials, use test credentials first and go through a permission/compliance review. Evidence: `.claude-plugin/plugin.json`, `.claude/commands/setup.md`, `CHANGELOG.md`, `CLAUDE.md` et al. - **Host AI context**: The AI Context Pack, Prompt Preview, Skill routing, risk rules, and project facts. Why: Importing context affects the host AI's later judgment, so avoid packaging unverified items as facts. ### Minimum Safe Next Steps - **Run Prompt Preview first**: Use an interactive trial to verify the task profile and role match first; do not import the whole role library up front. (applies when: Applies to any project, especially when output quality is unknown.) - **Trial-install only in an isolated directory or a test account**: Avoid letting install commands pollute your primary host AI, real projects, or home directory. (applies when: When there are signals of command execution, plugin config, or local writes.) - **Back up your host AI configuration first**: Skill, plugin, and rule files may change the default behavior of Claude/Cursor/Codex. (applies when: When there is a plugin manifest, a Skill, or a host rule entrypoint.) - **Do not use real production credentials**: Once an environment variable / API key enters the host or toolchain, it can create account and compliance risk. (applies when: When environment signals like API, TOKEN, KEY, or SECRET appear.) - **After install, verify just one minimal task**: Verify loading, compatibility, output quality, and rollback first, then decide whether to use it deeply. (applies when: When moving from a trial into a real workflow.) ### Exit Plan - **Preserve the pre-install state**: Record the original host config and project state so you can later judge whether it is recoverable. - **Be ready to remove the host plugin / Skill / rule entrypoint**: If behavior is off after the trial install, you can restore the host AI to its pre-trial state. - **Keep a record of the original role selection**: If output goes off-topic, you can return to the task-profiling stage and reselect a role instead of pushing on with the wrong one. - **Record the install commands and written paths**: Without clear uninstall instructions, you at least need to know which directories or configs to clean up manually. - **Be ready to revoke test API keys or tokens**: If test credentials leak or are misused, you can cut losses quickly. - **If there is no rollback path, do not enter your primary environment**: No rollback is a blocker before continuing; do not proceed on trust or luck. ## What Can Only Be Previewed - Explain who the project fits and what it can do - Demonstrate a typical conversation flow based on project docs - Help the user decide whether it is worth installing or researching further ## What Must Be Verified After Install - Actually installing the Skill, plugin, or CLI - Running scripts, modifying local files, or accessing external services - Verifying real output quality, performance, and compatibility ## Boundary & Risk Decision Card - **Mistaking the pre-install preview for a real run**: The user may overestimate how much configuration, permission, and compatibility verification the project has already done. Mitigation: Clearly separate prompt_preview_can_do from runtime_required. Claim: `clm_0008` inferred 0.45 - **Host AI plugin or Skill rule conflicts**: New rules may change how the user's existing host AI behaves. Mitigation: Inspect the plugin manifest and Skill files before installing, and test in isolation if needed. Evidence: `.claude-plugin/plugin.json` Claim: `clm_0009` supported 0.86 - **Command execution will modify the local environment**: Install commands may write to the user's home directory, the host plugin directory, or project configuration. Mitigation: Run in an isolated environment or a test account first. Evidence: `README.md` Claim: `clm_0010` supported 0.86 - **To confirm**: After a real install, is it compatible with the user's current host AI version?. Why: Compatibility can only be verified in the actual host environment. - **To confirm**: Does the project's output quality meet the user's specific task?. Why: The pre-install preview can only show flow and boundaries; it cannot replace real evaluation. - **To confirm**: Do the install commands require network access, permissions, or global writes?. Why: This affects install risk in both enterprise and personal environments. ## Pre-Work Working Context ### Loading Order - First read how_to_use.host_ai_instruction to establish the boundaries of this pre-install judgment asset. - Read claim_graph_summary to confirm facts come from the Claim/Evidence Graph, not the Human Wiki narrative. - Then read intended_users, capabilities, and quick_start_candidates to judge whether the user is a match. - When you need to carry out a concrete task, check role_skill_index first, then evidence_index. - For real install, file modification, network access, performance, or compatibility questions, turn to risk_card and boundaries.runtime_required. ### Task Routes - **Multi-Host Install and Distribution**: State that this is an after-install capability first, then give a pre-install checklist. Boundary: Must be verified after a real install or run. Evidence: `.claude-plugin/plugin.json` Claim: `clm_0001` supported 0.86 - **Command-Line Startup or Install Flow**: State that this is an after-install capability first, then give a pre-install checklist. Boundary: Must be verified after a real install or run. Evidence: `README.md` Claim: `clm_0002` supported 0.86 ### Context Scale - Total files: 104 - Important-file coverage: 40/104 - Evidence index entries: 60 - Role / Skill entries: 25 ### Handling Insufficient Evidence - **missing_evidence**: State that evidence is insufficient and ask the user for the target file, a README section, or after-install verification records; do not fill in facts. - **out_of_scope_request**: State that the task is beyond the current AI Context Pack's evidence scope and suggest the user check the Human Manual or verify after a real install. - **runtime_request**: Provide a pre-install checklist and command sources, but do not run commands for the user or claim they have been run. - **source_conflict**: Show the conflicting sources side by side, mark them as unverified, and do not force a single version. ## Prompt Recipes ### Fit assessment - Goal: Judge whether this project fits the user's current task. - Expected output: A fit conclusion, key reasons, evidence citations, what can be previewed before install, what must be verified after install, and a next-step recommendation. ```text Based on the AI Context Pack for omni-ai-mcp, ask me 3 necessary questions first, then judge whether it fits my task. The answer must cover: who it fits, what it can do, what it cannot do, whether it is worth installing, and where the evidence comes from. Every project fact must cite evidence_refs, source_paths, or a claim_id. ``` ### Pre-install experience - Goal: Let the user feel the core workflow before installing, while avoiding packaging the preview as real capability or a marketing promise. - Expected output: An experience script with boundary labels, an after-install verification checklist, and a cautious recommendation; with no real-run promises or strong marketing language. ```text Treat omni-ai-mcp as a pre-install experience asset, not an already-installed tool or a real runtime environment. Output exactly four parts: 1. Ask me 3 necessary questions first. 2. Give an "experience script": use the three labels [Previewable before install], [Must verify after install], and [Insufficient evidence] to show how it might guide the workflow. 3. Give an after-install verification checklist: list which capabilities can only be confirmed after a real install, real host loading, and a real project run. 4. Give a cautious recommendation: only "worth researching/trialing further", "add information before deciding", or "not recommended to continue"; do not endorse the project. Hard boundaries: - Do not claim you have installed, run, executed tests, modified files, or produced real results. - Do not write promise-like phrasing such as "auto-adapts", "guarantees passing", "perfect fit", or "strongly recommend installing". - If you describe how it works after install, you must use a conditional such as "if installed successfully and the host loads the Skill correctly, it might...". - The experience script may only be written as "example lines / hypothetical flow": use "might ask / might suggest / might show", not "has written, has generated, has passed, is running, is generating". - Prompt Preview does not hand out install commands; if the user is ready to trial, only prompt them to read Quick Start and the Risk Card first and to verify in an isolated environment. - Every project fact must come from a supported claim, evidence_refs, or source_paths; inferred/unverified items can only be risks or open questions. ``` ### Role / Skill selection - Goal: Pick the best-matching asset from the project's roles or Skills. - Expected output: A list of candidate roles or Skills, each with an applicable scenario, evidence paths, risk boundary, and whether after-install verification is needed. ```text Read role_skill_index and recommend 3-5 of the most relevant roles or Skills for my target task. For each recommendation, state the applicable scenario, likely output, risk boundary, and evidence_refs. ``` ### Risk pre-check - Goal: Identify environment, permission, rule-conflict, and quality risks before installing or adopting. - Expected output: A checklist of environment, permission, dependency, license, host-conflict, quality risk, and unknown items. ```text Based on risk_card, boundaries, and quick_start_candidates, give me a pre-install risk pre-check list. Do not run commands for me; only explain what I should check, why, and what impact a failure would have. ``` ### Host AI kickoff instruction - Goal: Turn the project context into a host AI instruction for the start of a conversation. - Expected output: A pre-work instruction with clear boundaries and clear evidence citations, suitable to copy to a host AI. ```text Based on the AI Context Pack for omni-ai-mcp, generate a pre-work instruction I can paste to my host AI. This instruction must obey not_runtime=true and must not claim the project has been installed, run, or produced real results. ``` ## Role / Skill Index - Indexed 25 role / Skill / project-doc entries. - **CLAUDE.md** (project_doc): This file provides context to Claude Code when working with this repository. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `CLAUDE.md` - **omni-ai-mcp** (project_doc): The complete AI bridge for Claude Code — Gemini's exclusive capabilities video, TTS, 1M context, RAG, Deep Research plus 400+ models via OpenRouter. One MCP server, every AI model, zero friction. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `README.md` - **Context is everything** (project_doc): Ask Gemini Pro anything with full context. Usage: /gemini Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini.md` - **Contributing to omni-ai-mcp** (project_doc): Thank you for your interest in contributing! This document provides guidelines for contributing to the project. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `CONTRIBUTING.md` - **Changelog** (project_doc): All notable changes to this project will be documented in this file. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `CHANGELOG.md` - **DOC GOVERNANCE — how to keep the documentation aligned generated by virgilio init** (project_doc): DOC GOVERNANCE — how to keep the documentation aligned generated by virgilio init Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `DOC_GOVERNANCE.md` - **Security Policy** (project_doc): Version Supported Notes ------- ------------------ ----- 3.0.x :white check mark: Current stable, security hardening 2.7.x :white check mark: Maintenance mode 2.6.x :white check mark: Maintenance mode < 2.6 :x: Upgrade recommended Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `SECURITY.md` - **cowork** (project_doc): Claude-Gemini co-working agent. Use when the user wants two independent AI perspectives on the same problem, needs a Gemini second opinion on Claude's analysis, wants adversarial validation of a solution, or says things like "what does Gemini think about this?", "double-check with Gemini", "get a second opinion", "verify this with another AI", or "cowork on this". Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/agents/cowork.md` - **gemini-analyzer** (project_doc): Codebase analysis specialist with 1M token context window. Use when the user asks to analyze, review, or audit a codebase, find security vulnerabilities, assess architecture, review large files that exceed normal context limits, or needs a comprehensive code audit across multiple files. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/agents/gemini-analyzer.md` - **gemini-researcher** (project_doc): Deep research specialist. Use when the user asks for comprehensive research on a topic, needs sources and citations, wants autonomous multi-step web research, or asks to "research", "investigate", or "find out about" something. Handles queries requiring 40+ sources or 5-60 minute autonomous research tasks. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/agents/gemini-researcher.md` - **model-orchestrator** (project_doc): Multi-model AI orchestrator. Use when the user wants to compare answers from different AI models, delegate a task to a specific model GPT-4o, Llama, Mistral, Gemini, Claude via OpenRouter , run the same prompt on multiple models, or says things like "ask GPT-4o", "use Llama for this", "compare how different models respond", "what would Gemini say about this", or "get a second opinion from another AI". Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/agents/model-orchestrator.md` - **Ask Model** (project_doc): Ask any AI model Gemini, GPT-4o, Llama, Mistral, Claude via OpenRouter . Usage: /ask-model model prompt . Examples: /ask-model gpt-4o explain quantum computing, /ask-model gemini what is RLHF Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/ask-model.md` - **Cowork** (project_doc): Claude + Gemini co-working on the same task for two independent perspectives. Usage: /cowork Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/cowork.md` - **Gemini Analyze** (project_doc): Analyze a codebase or directory with Gemini's 1M context window. Usage: /gemini-analyze path or files Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-analyze.md` - **Gemini Brainstorm** (project_doc): Structured brainstorming with 6 methodologies. Usage: /gemini-brainstorm Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-brainstorm.md` - **Gemini Challenge** (project_doc): Devil's Advocate - challenge an idea, plan, or code to find flaws. Usage: /gemini-challenge Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-challenge.md` - **Step 1 — Read and analyze the document** (project_doc): Enrich a document with infographics, diagrams, and illustrations generated by Gemini 3 Pro. Usage: /gemini-illustrate Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-illustrate.md` - **What Gemini 3 Pro image generation excels at** (project_doc): Generate infographics, technical diagrams, and illustrations using Gemini 3 Pro. Usage: /gemini-image Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-image.md` - **Gemini Models** (project_doc): List available AI models Gemini + OpenRouter . Usage: /gemini-models Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-models.md` - **Gemini Research** (project_doc): Autonomous deep research with 40+ sources 5-30 min . Usage: /gemini-research Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-research.md` - **Gemini Review** (project_doc): Code review with Gemini Pro. Usage: /gemini-review Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/gemini-review.md` - **Setup** (project_doc): Setup omni-ai-mcp: enter your API key in chat to configure everything automatically Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `.claude/commands/setup.md` - **virgilio's adapter/probe contract** (project_doc): An adapter is a file at adapters/ .mjs . It binds a question about reality to a way of observing it. It's the only layer that touches git / the filesystem / the network — the core doesn't know what reality is, only how to compare it against doc declarations. Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `virgilio/adapters/_contract.md` - **Playbook — virgilio audit semantic §9: keep the docs honest against reality** (project_doc): Playbook — virgilio audit semantic §9: keep the docs honest against reality Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `virgilio/modes/audit.md` - **Playbook — virgilio init lay the foundations** (project_doc): Playbook — virgilio init lay the foundations Activation hint: Reference this when the user needs to understand the project's structure, install path, or boundaries. Evidence: `virgilio/modes/init.md` ## Evidence Index - Indexed 60 evidence entries. - **CLAUDE.md** (documentation): This file provides context to Claude Code when working with this repository. Evidence: `CLAUDE.md` - **omni-ai-mcp** (documentation): The complete AI bridge for Claude Code — Gemini's exclusive capabilities video, TTS, 1M context, RAG, Deep Research plus 400+ models via OpenRouter. One MCP server, every AI model, zero friction. Evidence: `README.md` - **Context is everything** (documentation): Use the ask gemini MCP tool to answer the following request. Evidence: `.claude/commands/gemini.md` - **Package** (package_manifest): { "name": "virgilio", "version": "0.1.0", "description": "Reality-driven documentation/status governance gate — a stack-agnostic core + pluggable adapters. The guide that lays and maintains a project's foundations of truth.", "type": "module", "engines": { "node": " =18" }, "bin": { "virgilio": "./bin/cli.mjs" }, "scripts": { "check": "node bin/cli.mjs check", "report": "node bin/cli.mjs report", "bite": "node bin/cli.mjs bite", "bite:all": "node bin/cli.mjs bite --all", "test": "node test/portability.test.mjs && node test/playbook-cli.test.mjs && node bin/cli.mjs bite --all" }, "dependencies": {} } Evidence: `virgilio/package.json` - **Contributing to omni-ai-mcp** (documentation): Thank you for your interest in contributing! This document provides guidelines for contributing to the project. Evidence: `CONTRIBUTING.md` - **Plugin** (structured_config): { "name": "omni-ai-mcp", "version": "4.0.6", "description": "Multi-AI MCP bridge: Gemini + 400+ models via OpenRouter. 20 tools: text, code, image, video, TTS, RAG, Deep Research.", "author": { "name": "Marco Armellino", "url": "https://github.com/marmyx77" }, "license": "MIT", "homepage": "https://github.com/marmyx77/omni-ai-mcp", "repository": "https://github.com/marmyx77/omni-ai-mcp", "keywords": "gemini", "openrouter", "mcp", "ai", "multimodal", "image", "video", "tts", "research" , "mcpServers": { "omni-ai-mcp": { "command": "uvx", "args": "omni-ai-mcp" , "env": { "GEMINI API KEY": "${GEMINI API KEY}", "OPENROUTER API KEY": "${OPENROUTER API KEY}" } } } } Evidence: `.claude-plugin/plugin.json` - **License** (source_file): Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files the "Software" , to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: Evidence: `LICENSE` - **=============================================================================** (source_file): mcp = FastMCP ⋮---- @mcp.tool def gemini create file store name: str - str ⋮---- @mcp.tool def gemini upload file file path: str, store name: str - str ⋮---- @mcp.tool def gemini list file stores - str ⋮---- @mcp.tool def gemini list models include openrouter: bool = True - str ⋮---- """ Advanced brainstorming with multiple methodologies. Uses Gemini 3 Pro for creative reasoning with structured frameworks. Args: topic: Topic or challenge to brainstorm methodology: auto, divergent, convergent, scamper, design-thinking, lateral domain: software, business, creative, marketing, product, research constraints: Known limitations budget, time, technical, legal context: Additional context or backgro… Evidence: `app/server.py` - **Docker Compose** (source_file): services: gemini-mcp: build: . container name: omni-ai-mcp restart: unless-stopped environment: - GEMINI API KEY=${GEMINI API KEY} - GEMINI SANDBOX ROOT=/workspace - GEMINI SANDBOX ENABLED=true - GEMINI ACTIVITY LOG=true - GEMINI LOG DIR=/logs - GEMINI MAX FILE SIZE=102400 volumes: - ./workspace:/workspace:rw - ./logs:/logs:rw - ./backups:/workspace/.gemini backups:rw security opt: - no-new-privileges:true read only: true tmpfs: - /tmp:size=100M deploy: resources: limits: cpus: '2' memory: 2G reservations: cpus: '0.5' memory: 512M logging: driver: json-file options: max-size: "10m" max-file: "3" logs-viewer: image: amir20/dozzle:latest container name: gemini-logs-viewer volumes: - /var/run/… Evidence: `docker-compose.yml` - **Example plugin entry point for third-party plugins** (source_file): build-system requires = "hatchling" build-backend = "hatchling.build" Evidence: `pyproject.toml` - **Requirements** (source_file): mcp cli =1.0.0 google-genai =2.0.0 pydantic =2.0.0 defusedxml =0.7.1 filelock =3.0.0 Evidence: `requirements.txt` - **Check for API key** (source_file): Check for API key ⋮---- server dir = os.path.dirname os.path.abspath file Evidence: `run.py` - **Check if API key was provided** (source_file): set -e GREEN='\033 0;32m' RED='\033 0;31m' BLUE='\033 0;34m' YELLOW='\033 1;33m' NC='\033 0m' echo -e "${BLUE}╔════════════════════════════════════════════╗${NC}" echo -e "${BLUE}║ omni-ai-mcp Setup v4.0.0 ║${NC}" echo -e "${BLUE}║ Gemini + OpenRouter — 20 AI Tools ║${NC}" echo -e "${BLUE}╚════════════════════════════════════════════╝${NC}" echo "" Check if API key was provided API KEY="$1" OPENROUTER KEY="${2:-}" if -z "$API KEY" ; then echo -e "${RED}Error: Please provide your Gemini API key${NC}" echo "" echo "Usage: ./setup.sh YOUR GEMINI API KEY OPENROUTER API KEY " echo "" echo " Gemini key required : https://aistudio.google.com/apikey" echo " OpenRouter key optional, for 400+ models… Evidence: `setup.sh` - **Model Versions can be overridden via environment variables** (source_file): @dataclass class Config ⋮---- version: str = "4.5.0" ⋮---- api key: str = field default factory=lambda: os.environ.get "GEMINI API KEY", "" ⋮---- Model Versions can be overridden via environment variables Text Generation Models model pro: str = field model flash: str = field ⋮---- model image pro: str = field model image flash: str = field ⋮---- model veo31: str = field model veo31 fast: str = field model veo3: str = field model veo3 fast: str = field model veo2: str = field ⋮---- model tts flash: str = field model tts pro: str = field ⋮---- model deep research: str = field ⋮---- openrouter api key: str = field openrouter default model: str = field openrouter timeout: int = field ⋮---- conv… Evidence: `app/core/config.py` - **Text format file logging** (source_file): @dataclass class LogRecord ⋮---- timestamp: str level: str tool: Optional str status: str duration ms: Optional float request id: Optional str details: Dict str, Any error: Optional str ⋮---- class StructuredLogger ⋮---- def init self, name: str = "omni-ai-mcp" ⋮---- def emit self, record: LogRecord ⋮---- safe details = {} ⋮---- str val = str v if not isinstance v, str else v ⋮---- output = { ⋮---- output = {k: v for k, v in output.items if v is not None} ⋮---- def tool start self, tool: str, request id: str, args: Dict ⋮---- def tool success self, tool: str, request id: str, duration ms: float, result len: int ⋮---- def tool error self, tool: str, request id: str, duration ms: float, error… Evidence: `app/core/logging.py` - **Check if within sandbox** (source_file): HAS FILELOCK = True ⋮---- HAS FILELOCK = False ⋮---- HAS FCNTL = True ⋮---- HAS FCNTL = False ⋮---- class FileLockError Exception ⋮---- @contextmanager def file lock file path: str, timeout: float = 5.0, exclusive: bool = True ⋮---- lock path = f"{file path}.lock" ⋮---- lock dir = os.path.dirname lock path ⋮---- locker = FileLocker lock path ⋮---- lock fd = None ⋮---- lock fd = os.open lock path, os.O CREAT os.O RDWR, 0o600 ⋮---- start time = time.time lock type = fcntl.LOCK EX if exclusive else fcntl.LOCK SH ⋮---- class RegexTimeoutError Exception ⋮---- @contextmanager def regex timeout seconds: float = 1.0 ⋮---- def timeout handler signum, frame ⋮---- old handler = signal.signal signal.SI… Evidence: `app/core/security.py` - **Inputs** (source_file): PYDANTIC AVAILABLE = True ⋮---- PYDANTIC AVAILABLE = False ⋮---- class BaseModel def Field args, kwargs def field validator args, kwargs ⋮---- def decorator func ⋮---- class ThinkingLevel str, Enum ⋮---- OFF = "off" LOW = "low" HIGH = "high" ⋮---- class CodeStyle str, Enum ⋮---- PRODUCTION = "production" PROTOTYPE = "prototype" MINIMAL = "minimal" ⋮---- class AnalysisType str, Enum ⋮---- ARCHITECTURE = "architecture" SECURITY = "security" REFACTORING = "refactoring" DOCUMENTATION = "documentation" DEPENDENCIES = "dependencies" GENERAL = "general" ⋮---- class ChallengeFocus str, Enum ⋮---- PERFORMANCE = "performance" MAINTAINABILITY = "maintainability" SCALABILITY = "scalability" COST = "cos… Evidence: `app/schemas/inputs.py` - **Gemini** (source_file): MODELS = { ⋮---- IMAGE MODELS = { ⋮---- VIDEO MODELS = { ⋮---- TTS MODELS = { ⋮---- TTS VOICES = { ⋮---- client = None types = None error: Optional str = None available: bool = False ⋮---- types = genai types ⋮---- API KEY = config.api key ⋮---- error = "Please set GEMINI API KEY environment variable" ⋮---- client = genai.Client api key=API KEY available = True ⋮---- error = "google-genai SDK not installed. Run: pip install google-genai" ⋮---- error = "Failed to initialize Gemini client. Check your API key." ⋮---- def is available - bool ⋮---- def get error - Optional str ⋮---- error msg = str e .lower ⋮---- flash model = MODELS "flash" ⋮---- response = client.models.generate content model=… Evidence: `app/services/gemini.py` - **Model Registry** (source_file): CATEGORY PRIORITIES: Dict str, List str = { ⋮---- STATIC FALLBACKS: Dict str, str = { ⋮---- CACHE TTL = 3600 ⋮---- class ModelRegistry ⋮---- def init self - None ⋮---- def is cache valid self - bool ⋮---- def refresh cache self - None ⋮---- models = client.models.list ⋮---- self. resolved.clear Invalidate resolved cache on refresh ⋮---- def ensure fresh self - None ⋮---- def resolve self, category: str - str ⋮---- candidates = CATEGORY PRIORITIES.get category, available = set self. available model names or ⋮---- fallback = STATIC FALLBACKS.get category, "gemini-2.5-flash" ⋮---- def list available self - Dict str, object ⋮---- """Return all available models per category and deprecation warni… Evidence: `app/services/model_registry.py` - **Openrouter** (source_file): BASE URL = "https://openrouter.ai/api/v1" MODELS CACHE TTL = 3600 DEFAULT TIMEOUT = 120 METADATA TIMEOUT = 30 ⋮---- def extract citations result: Dict str, Any - List str ⋮---- top level = ⋮---- choices = result.get "choices" or message = choices 0 .get "message" or {} if choices else {} annotated = ⋮---- def append sources text: str, citations: List str - str ⋮---- sources = "\n".join f"{i}. {url}" for i, url in enumerate citations, 1 ⋮---- class OpenRouterClient ⋮---- """ Client for OpenRouter API OpenAI-compatible . All methods silently return empty results when the API key is not configured. """ ⋮---- @property def is available self - bool ⋮---- """True if an API key is configured.""" ⋮… Evidence: `app/services/openrouter.py` - **Iterate newest to oldest to keep recent context when truncating** (source_file): DB FILE PERMISSIONS = stat.S IRUSR stat.S IWUSR ⋮---- DB DIR = Path.home / ".omni-ai-mcp" DB PATH = DB DIR / "conversations.db" ⋮---- CONVERSATION TTL HOURS = config.conversation ttl hours CONVERSATION MAX TURNS = config.conversation max turns ⋮---- @dataclass class ConversationTurn ⋮---- role: str content: str timestamp: str tool name: str files: List str ⋮---- def to dict self - Dict str, Any ⋮---- @classmethod def from row cls, row: tuple - "ConversationTurn" ⋮---- class PersistentConversationMemory ⋮---- def init db self ⋮---- db exists = self.db path.exists ⋮---- wal path = Path f"{self.db path}-wal" shm path = Path f"{self.db path}-shm" ⋮---- @contextmanager def get connection self ⋮-… Evidence: `app/services/persistence.py` - **Generate schema if not provided** (source_file): PYDANTIC AVAILABLE = True ⋮---- PYDANTIC AVAILABLE = False BaseModel = None ⋮---- @dataclass class ToolDefinition ⋮---- name: str description: str handler: Callable input schema: Dict str, Any input model: Optional Type = None tags: List str = field default factory=list ⋮---- class ToolRegistry ⋮---- def init self ⋮---- description = handler. doc or f"Tool: {name}" description = description.strip .split '\n' 0 First line only ⋮---- Generate schema if not provided ⋮---- input schema = self. generate schema handler, input model ⋮---- def generate schema self, handler: Callable, input model: Type = None - Dict str, Any ⋮---- """Generate JSON schema from function signature or Pydantic model."""… Evidence: `app/tools/registry.py` - **Extract file references from expanded prompt for tracking** (source_file): ASK GEMINI SCHEMA = { ⋮---- mode = "cloud" ⋮---- original prompt = prompt prompt = expand file references prompt ⋮---- size error = check prompt size prompt ⋮---- Extract file references from expanded prompt for tracking files referenced = ⋮---- file refs = re.findall r' ?