# codegraphcontext - 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 codegraphcontext. 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 - **Users who want to bring professional workflows into a host AI**: The repo contains Skill documents. Evidence: `.cursor/skills/codegraphcontext/SKILL.md` Claim: `clm_0004` supported 0.86 ## What It Can Do - **AI Skill / Agent Instruction Asset Library** (Previewable before install): The project contains Skill or Agent instruction files that a host AI can read, useful for bringing professional workflows into hosts like Claude, Codex, or Cursor. Evidence: `.cursor/skills/codegraphcontext/SKILL.md` 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 codegraphcontext` Evidence: `README.md` Claim: `clm_0005` supported 0.86 - `curl -sSL https://raw.githubusercontent.com/CodeGraphContext/CodeGraphContext/main/scripts/post_install_fix.sh | bash` Evidence: `README.md` Claim: `clm_0006` supported 0.86 ## Continue-or-Stop Decision Card - **Current recommendation**: Sandbox trial only - **Why**: The project has signals of install commands, host configuration, or local writes; do not go straight into your primary environment—trial it in isolation first. ### 30-Second Read - **What to do now**: Sandbox trial only - **Minimum safe next step**: Run Prompt Preview first; if you still want to install, trial only in an isolated environment - **Do not trust yet**: Real output quality cannot be trusted before install. - **Continuing will touch**: Command execution, Host AI configuration, Local environment or project files ### 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 - **Target-audience signal: Users who want to bring professional workflows into a host AI** (supported): Backed by a supported claim or project evidence, but that still is not the same as real install results. Evidence: `.cursor/skills/codegraphcontext/SKILL.md` Claim: `clm_0004` supported 0.86 - **Capability exists: AI Skill / Agent Instruction Asset Library** (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: `.cursor/skills/codegraphcontext/SKILL.md` 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_0005` supported 0.86 ### What You Cannot Trust Yet - **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: `.cursor/skills/codegraphcontext/SKILL.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. - **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. - **Do the install commands require network access, permissions, or global writes?** (unverified): This affects install risk in both enterprise and personal environments. Evidence: `README.md` ### What Continuing Will Touch - **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: `.cursor/skills/codegraphcontext/SKILL.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: `README.md` - **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 a pre-install interactive trial to judge whether the way of working fits; it needs no authorization or environment change. (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.) - **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. - **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. - **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_0007` inferred 0.45 - **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_0008` 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 - **AI Skill / Agent Instruction Asset Library**: Use role_skill_index / evidence_index to help the user pick a usable role, Skill, or workflow first. Boundary: Can be experienced via a pre-install Prompt. Evidence: `.cursor/skills/codegraphcontext/SKILL.md` 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: 366 - Important-file coverage: 40/366 - Evidence index entries: 80 - Role / Skill entries: 1 ### 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 codegraphcontext, 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 codegraphcontext 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 codegraphcontext, 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 1 role / Skill / project-doc entries. - **codegraphcontext** (skill): - Activation hint: When the user's task is highly relevant to the workflow described by “codegraphcontext”, use it for a pre-install experience first, then decide whether to install. Evidence: `.cursor/skills/codegraphcontext/SKILL.md` ## Evidence Index - Indexed 80 evidence entries. - **Contributing to CodeGraphContext** (documentation): Thank you for your interest in contributing to CodeGraphContext CGC . We welcome contributions from the community to improve the performance, language support, and tooling capabilities of the engine. Evidence: `docs/docs/contributing.md` - **🏗️ CodeGraphContext CGC** (documentation): Turn code repositories into a queryable graph for AI agents. Evidence: `README.md` - **Kubernetes Deployment for CodeGraphContext** (documentation): Kubernetes Deployment for CodeGraphContext Evidence: `k8s/README.md` - **Website** (documentation): This directory contains the source code for the CodeGraphContext website. Evidence: `website/README.md` - **CodeGraphContext VS Code Extension** (documentation): CodeGraphContext brings graph-native code intelligence into VS Code through CGC MCP. Evidence: `extensions/vscode/README.md` - **Package** (package_manifest): { "name": "vite react shadcn ts", "private": true, "version": "0.0.0", "type": "module", "scripts": { "dev": "vite", "build": "vite build", "build:dev": "vite build --mode development", "lint": "eslint .", "preview": "vite preview" }, "dependencies": { "@hookform/resolvers": "^3.10.0", "@radix-ui/react-accordion": "^1.2.11", "@radix-ui/react-alert-dialog": "^1.1.14", "@radix-ui/react-aspect-ratio": "^1.1.7", "@radix-ui/react-avatar": "^1.1.10", "@radix-ui/react-checkbox": "^1.3.2", "@radix-ui/react-collapsible": "^1.1.11", "@radix-ui/react-context-menu": "^2.2.15", "@radix-ui/react-dialog": "^1.1.14", "@radix-ui/react-dropdown-menu": "^2.1.15", "@radix-ui/react-hover-card": "^1.1.14", "@rad… Evidence: `website/package.json` - **Package** (package_manifest): { "name": "codegraphcontext-vscode", "displayName": "CodeGraphContext", "description": "Code graph intelligence in VS Code powered by CGC MCP.", "version": "0.1.0", "publisher": "ShashankShekharSingh", "engines": { "vscode": "^1.86.0" }, "categories": "Other", "Programming Languages" , "keywords": "code graph", "MCP", "call graph", "complexity", "code analysis", "dead code", "graph intelligence" , "galleryBanner": { "color": " 1e1e2e", "theme": "dark" }, "activationEvents": "onCommand:cgc.openDashboard", "onCommand:cgc.showCallGraph", "onCommand:cgc.analyzeRelationships", "onCommand:cgc.refreshIndex", "onCommand:cgc.openEngineConfig", "onCommand:cgc.showVariableImpact", "onCommand:cgc.showC… Evidence: `extensions/vscode/package.json` - **CodeGraphContext CGC** (skill_instruction): - Indexing or re-indexing a codebase for AI or CLI queries. - Explaining how to install and run cgc , choose a database backend, or wire MCP into an editor. - Interpreting CGC tools: codegraph find code , analyze code relationships , add code to graph , etc. Evidence: `.cursor/skills/codegraphcontext/SKILL.md` - **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` - **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: `extensions/vscode/LICENSE` - **CodeGraphContext CGC — Complete Architecture Document** (documentation): CodeGraphContext CGC — Complete Architecture Document Evidence: `docs/ARCHITECTURE.md` - **CGC Bundle System Architecture** (documentation): ┌─────────────────────────────────────────────────────────────────┐ │ CGC Bundle Ecosystem │ └─────────────────────────────────────────────────────────────────┘ Evidence: `docs/BUNDLE_ARCHITECTURE.md` - **CGC Bundle System - Bug Fixes Applied** (documentation): CGC Bundle System - Bug Fixes Applied Evidence: `docs/BUNDLE_BUGFIXES.md` - **CGC Bundle System - Implementation Summary** (documentation): CGC Bundle System - Implementation Summary Evidence: `docs/BUNDLE_IMPLEMENTATION.md` - **CGC Bundle Quick Reference** (documentation): bash Load a pre-indexed bundle cgc load numpy.cgc Evidence: `docs/BUNDLE_QUICKREF.md` - **🎉 CGC Bundle System - Complete Implementation** (documentation): 🎉 CGC Bundle System - Complete Implementation Evidence: `docs/BUNDLE_SUMMARY.md` - **Complete CLI Command Reference** (documentation): All CodeGraphContext CLI Commands - Comprehensive List Evidence: `docs/CLI_COMPLETE_REFERENCE.md` - **Integrating CodeGraphContext with ChatGPT and Claude** (documentation): Integrating CodeGraphContext with ChatGPT and Claude Evidence: `docs/INTEGRATION_GUIDE.md` - **MCP Tools Reference** (documentation): This document describes the Model Context Protocol MCP tools exposed by CodeGraphContext 0.4.16 . The server registers the full catalog defined in src/codegraphcontext/tool definitions.py same tools the CLI graph operations rely on . MCP tools/list returns 25 tool definitions—the subsections below enumerate each one. Evidence: `docs/MCP_TOOLS.md` - **On-Demand Bundle Generation Setup** (documentation): This guide explains how to set up and use the on-demand bundle generation feature. Evidence: `docs/ON_DEMAND_BUNDLES.md` - **🚀 Quick Reference - Bundle Registry Commands** (documentation): 🚀 Quick Reference - Bundle Registry Commands Evidence: `docs/QUICK_REFERENCE.md` - **CodeGraphContext Team Boundaries & Issue Routing Guide** (documentation): CodeGraphContext Team Boundaries & Issue Routing Guide Evidence: `docs/TEAM_ROLES.md` - **📚 Documentation Update Guide** (documentation): This guide explains how to update the CodeGraphContext documentation website. Evidence: `docs/UPDATING_DOCS.md` - **System Architecture** (documentation): CodeGraphContext CGC is structured as a multi-tier code intelligence pipeline. It acts as the bridge between source code parsers, local/remote graph databases, and client developer tools or AI agents. Evidence: `docs/docs/concepts/architecture.md` - **Database Backends** (documentation): CodeGraphContext CGC implements a pluggable database architecture. A common interface abstracts graph creation, updates, and traversals, allowing you to choose the database engine that best fits your scale, operating system, and visualization needs. Evidence: `docs/docs/concepts/backends.md` - **The Code Graph Model** (documentation): CodeGraphContext models codebase structures as a directed, attributed Property Graph . By mapping files, modules, classes, and functions to distinct nodes, and their interactions to directed edges, the engine provides a semantic representation of your code. Evidence: `docs/docs/concepts/graph-model.md` - **How Ingestion Works** (documentation): This guide explains how CodeGraphContext CGC parses source files, maps structural relationships, updates indices incrementally, and serves queries. Evidence: `docs/docs/concepts/how-it-works.md` - **Adding Language Support** (documentation): This guide outlines the steps required to add parsing support for a new programming language to CodeGraphContext. Evidence: `docs/docs/contributing_languages.md` - **Ingesting & Installing CodeGraphContext** (documentation): Ingesting & Installing CodeGraphContext Evidence: `docs/docs/getting-started/installation.md` - **Model Context Protocol Setup** (documentation): CodeGraphContext CGC implements the Model Context Protocol MCP . This enables LLM-powered applications and IDE extensions to discover and invoke tools that fetch context directly from your code graph. Evidence: `docs/docs/getting-started/mcp-setup.md` - **System Prerequisites** (documentation): CodeGraphContext CGC is designed as a client-server architecture. To ensure a successful installation, understand the primary roles and requirements of the environment. Evidence: `docs/docs/getting-started/prerequisites.md` - **Quickstart Guide** (documentation): This guide describes how to index a local repository and run your first code structure analysis queries. Evidence: `docs/docs/getting-started/quickstart.md` - **Portable CGC Bundles & Registries** (documentation): CodeGraphContext CGC supports Portable Graph Bundles .cgc files —serialized snapshots of an indexed codebase. Bundles allow teams to distribute pre-parsed code structures so that other developers or CI runners can load them without re-parsing the original source code. Evidence: `docs/docs/guides/bundles.md` - **Configuration Contexts & Workspaces** (documentation): Configuration Contexts & Workspaces Evidence: `docs/docs/guides/contexts.md` - **Ingesting Database & Cache Schemas** (documentation): CodeGraphContext CGC goes beyond parsing code syntax—it allows developers to ingest database and cache schemas. By linking code functions to database columns or cache keys, CGC maps dependencies from the API layer down to the storage tables. Evidence: `docs/docs/guides/datasource-indexing.md` - **Indexing Source Code** (documentation): Indexing extracts syntactic structures and links semantic relationships within a codebase to populate the graph database. CodeGraphContext CGC supports multiple scan strategies. Evidence: `docs/docs/guides/indexing.md` - **Developer Onboarding & Code Tour** (documentation): Welcome to the CodeGraphContext CGC developer portal. This guide details the structural layout of the repository to help new contributors navigate the codebase, understand the interactions between components, and locate files when debugging or extending features. Evidence: `docs/docs/guides/onboarding-codebase.md` - **Interactive Graph Visualization** (documentation): Visualizing your code graph helps identify complex call paths, cyclical dependencies, and architectural anomalies. CodeGraphContext includes a built-in React-based interactive force-directed graph visualizer. Evidence: `docs/docs/guides/visualization.md` - **Introduction to CodeGraphContext** (documentation): CodeGraphContext CGC is a high-performance, developer-focused Code Intelligence Engine that transforms source repositories into semantic, queryable property graphs. Tree-sitter and optional SCIP indexers extract symbols; CGC resolves calls, imports, and inheritance into a graph you can query from the CLI, MCP tools, or the HTTP API gateway. Evidence: `docs/docs/index.md` - **License** (documentation): CodeGraphContext is licensed under the MIT License. Evidence: `docs/docs/license.md` - **HTTP API Reference** (documentation): CodeGraphContext ships a CGC Gateway HTTP server for ChatGPT Actions, Claude connectors, and custom web frontends. It wraps the same MCP tool surface exposed by cgc mcp start . Evidence: `docs/docs/reference/api.md` - **CLI Command Reference** (documentation): The cgc command-line interface is the entry point for indexing code, running graph queries, managing contexts, and administering database backends. Evidence: `docs/docs/reference/cli.md` - **Configuration Reference** (documentation): CodeGraphContext CGC is configured using environment variables, local configuration files, and the CLI. Evidence: `docs/docs/reference/config.md` - **MCP Tool Reference** (documentation): When running the CodeGraphContext CGC MCP server, it registers a suite of 25 JSON-RPC tools that AI assistants can use to analyze and query the code graph. Evidence: `docs/docs/reference/mcp.md` - **Troubleshooting Manual** (documentation): This guide detail procedures for identifying, diagnosing, and resolving issues when setting up or executing CodeGraphContext. Evidence: `docs/docs/reference/troubleshooting.md` - **CodeGraphContext: 6-Month Evolution & Feature Roadmap** (documentation): CodeGraphContext: 6-Month Evolution & Feature Roadmap Evidence: `docs/docs/roadmap.md` - **🏗️ CodeGraphContext CGC** (documentation): コードリポジトリを AI エージェントが問い合わせ可能なグラフに変換します。 Evidence: `docs/translations/README.ja.md` - **🏗️ CodeGraphContext CGC** (documentation): 코드 저장소를 AI 에이전트가 쿼리할 수 있는 그래프로 변환합니다. Evidence: `docs/translations/README.kor.md` - **🏗️ CodeGraphContext CGC** (documentation): Превратите репозитории кода в запрашиваемый граф для ИИ-агентов. Evidence: `docs/translations/README.ru-RU.md` - **🏗️ CodeGraphContext CGC** (documentation): Перетворюйте кодові репозиторії на граф, який можуть запитувати AI-агенти. Evidence: `docs/translations/README.uk.md` - **🏗️ CodeGraphContext CGC** (documentation): 🌐 语言 Languages : - 🇬🇧 English README.md - 🇨🇳 中文 README.zh-CN.md - 🇰🇷 한국어 README.kor.md - 🇺🇦 Українська README.uk.md - 🇷🇺 Русский README.ru-RU.md - 🇯🇵 日本語 README.ja.md - 🇪🇸 Español 即将推出 Evidence: `docs/translations/README.zh-CN.md` - **🧪 CodeGraphContext Testing Strategy** (documentation): 🧪 CodeGraphContext Testing Strategy Evidence: `docs/TESTING.md` - **CodeGraphContext v0.3.8 — E2E Test Report Post-Merge PR 796 UNWIND Batching** (documentation): CodeGraphContext v0.3.8 — E2E Test Report Post-Merge PR 796 UNWIND Batching Evidence: `docs/test_report.md` - **CGC Call Graph Audit Report** (documentation): Generated: 2026-06-11 13:14 UTC CGC environment: { "python": "3.12.3 main, Mar 23 2026, 19:04:32 GCC 13.3.0 ", "projects": 21, "cgc version": "local" } Evidence: `CGC_CALL_GRAPH_AUDIT_REPORT.md` - **CGC E2E Bug Report** (documentation): - Date: 2026-06-09 manual subprocess execution - CGC version: 0.4.16 — tested on PyPI pip install codegraphcontext and local editable pip install -e . from this repo, uncommitted working-tree changes - Python: 3.12.3 - OS: Linux 6.8.0-124-generic - Method: Subprocess-only per E2E plan .cursor/plans/cgc e2e bug hunt 6028a5c6.plan.md . No source modifications. Tests run from /tmp with isolated HOME unless testing repo-local config bleed. - Harness note: Initial automated harness /tmp/cgc e2e harness.py produced many false positives truncated Rich output, missing bundle import -y , config bleed when cwd inside CGC repo . Findings below are manually verified. Evidence: `CGC_E2E_BUG_REPORT.md` - **CGC Graph Building Inconsistencies 100 items** (documentation): CGC Graph Building Inconsistencies 100 items Evidence: `CGC_GRAPH_INCONSISTENCIES.md` - **CGC Report** (documentation): God Nodes — Highest Fan-In These nodes are called from many places. High fan-in increases risk: a change here affects every caller. Evidence: `CGC_REPORT.md` - **CodeGraphContext: Master Roadmap & Engineering Audit** (documentation): CodeGraphContext: Master Roadmap & Engineering Audit Evidence: `ROADMAP.md` - **Components** (structured_config): { "$schema": "https://ui.shadcn.com/schema.json", "style": "default", "rsc": false, "tsx": true, "tailwind": { "config": "tailwind.config.ts", "css": "src/index.css", "baseColor": "slate", "cssVariables": true, "prefix": "" }, "iconLibrary": "lucide", "aliases": { "components": "@/components", "utils": "@/lib/utils", "ui": "@/components/ui", "lib": "@/lib", "hooks": "@/hooks" }, "registries": { "@magicui": "https://magicui.design/r/{name}.json" } } Evidence: `website/components.json` - **Tsconfig.App** (structured_config): { "compilerOptions": { "target": "ES2020", "useDefineForClassFields": true, "lib": "ES2020", "DOM", "DOM.Iterable" , "module": "ESNext", "skipLibCheck": true, Evidence: `website/tsconfig.app.json` - The remaining 20 evidence entries are in `AI_CONTEXT_PACK.json` or `EVIDENCE_INDEX.json`. ## Rules the Host AI Must Follow - **Treat this asset as pre-work context, not a runtime environment.**: The AI Context Pack contains only an evidence-backed understanding of the project, not the project's executable state. Evidence: `docs/docs/contributing.md`, `README.md`, `k8s/README.md` - **When answering the user, distinguish what can be previewed from what can only be verified after install.**: The consumer value of the pre-install experience comes from reducing bad installs and misjudgments, not from pretending to be a real run. Evidence: `docs/docs/contributing.md`, `README.md`, `k8s/README.md` ## Questions the User Should Answer First - Which host AI or local environment do you plan to use it in? - Do you just want to experience the workflow first, or are you ready to actually install? - What matters most to you: install cost, output quality, or conflicts with your existing rules? ## Acceptance Checks - Every capability claim can be traced back to a file path in evidence_refs. - AI_CONTEXT_PACK.md does not package previews as a real run. - The user can understand who it fits, what it can do, how to start, and the risk boundaries within 3 minutes. --- ## Doramagic Context Augmentation The following sections strengthen the repository context for a host AI. Human Manual data is a reading route, and pitfall notes become operating constraints. ## Human Manual Outline Usage rule: this is only a reading route and salience signal, not factual authority. Concrete claims must still return to repo evidence or Claim Graph. Host AI hard rules: - Do not treat page titles, section order, summaries, or importance values as factual project evidence. - When explaining the Human Manual outline, state that it is only a reading route or salience signal. - Capability, installation, compatibility, runtime state, and risk claims must cite repo evidence, source paths, or Claim Graph. - **Overview & System Architecture**: importance `high` - source_paths: README.md, docs/docs/concepts/architecture.md, docs/docs/concepts/how-it-works.md, docs/docs/index.md, pyproject.toml - **CLI Toolkit, MCP Server & VS Code Extension**: importance `high` - source_paths: src/codegraphcontext/cli/main.py, src/codegraphcontext/cli/setup_wizard.py, src/codegraphcontext/cli/config_manager.py, src/codegraphcontext/cli/registry_commands.py, src/codegraphcontext/cli/visualizer.py - **Language Parsers & Indexing Pipeline**: importance `high` - source_paths: src/codegraphcontext/tools/tree_sitter_parser.py, src/codegraphcontext/tools/graph_builder.py, src/codegraphcontext/tools/indexing/pipeline.py, src/codegraphcontext/tools/indexing/discovery.py, src/codegraphcontext/tools/indexing/pre_scan.py - **Database Backends, Data Model, Bundles & Visualization**: importance `high` - source_paths: src/codegraphcontext/core/database.py, src/codegraphcontext/core/database_falkordb.py, src/codegraphcontext/core/database_falkordb_remote.py, src/codegraphcontext/core/database_kuzu.py, src/codegraphcontext/core/database_embedded_kuzu.py ## Repo Inspection Evidence - repo_clone_verified: true - repo_inspection_verified: true - repo_commit: `8bd1a8f7214a3bdb2788106a6949c60dd83dd5be` - inspected_files: `Dockerfile`, `README.md`, `pyproject.toml`, `docs/ARCHITECTURE.md`, `docs/BUNDLES.md`, `docs/BUNDLE_ARCHITECTURE.md`, `docs/BUNDLE_BUGFIXES.md`, `docs/BUNDLE_IMPLEMENTATION.md`, `docs/BUNDLE_QUICKREF.md`, `docs/BUNDLE_SUMMARY.md`, `docs/CLI_COMPLETE_REFERENCE.md`, `docs/INTEGRATION_GUIDE.md`, `docs/MCP_TOOLS.md`, `docs/ON_DEMAND_BUNDLES.md`, `docs/QUICK_REFERENCE.md`, `docs/TEAM_ROLES.md`, `docs/TESTING.md`, `docs/UPDATING_DOCS.md`, `docs/docs/concepts/architecture.md`, `docs/docs/concepts/backends.md` Host AI hard rules: - Without repo_clone_verified=true, do not claim that the source code has been read. - Without repo_inspection_verified=true, do not write README, docs, or package-file conclusions as facts. - Without quick_start_verified=true, do not claim that the Quick Start path has run successfully. ## Doramagic Pitfall Constraints These rules come from Doramagic discovery, validation, or compilation findings. The host AI must treat them as operating constraints, not background notes. - No project-specific pitfall log has English-native content; rely on the base pack's risk card and evidence index. Do not invent missing facts.