codegraph-ai
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@codegraph-aiget context for getAllServers"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
CodeGraph (codegraph-ai)
Context engine for AI coding agents. Parses your codebase with tree-sitter, builds a dependency graph, and serves structured context via MCP.
npm package:
codegraph-ai— install withnpx codegraph-ai
Works with: Claude Code, Cursor, Windsurf, Cline, and any MCP-compatible client.
Result: Your AI agent gets pre-analyzed context instead of reading raw files. 96% fewer tokens on average.
Token savings (real benchmark)
Tested on a production Next.js project (82 files, 384 symbols):
Scenario | Without | With CodeGraph | Reduction |
Understand | 19,220 tk | 637 tk | 97% |
Understand | 40,742 tk | 1,736 tk | 96% |
Search for "server" | 4,716 tk | 475 tk | 90% |
Understand project structure | 15,145 tk | 1,047 tk | 93% |
Total (8 operations) | 126,488 tk | 5,558 tk | 96% |
At 100 operations/day: ~$136/month saved on API costs.
Run the benchmark yourself: npx tsx src/benchmark.ts /path/to/project
Related MCP server: contextsliver
How it works
Your codebase
│
▼
[1. INDEX] tree-sitter parses every file
│ extracts: functions, classes, imports, exports, types
▼
[2. GRAPH] resolves imports between files
│ builds graph: node = symbol, edge = "uses/imports"
▼
[3. STORE] SQLite + FTS5 full-text search (.codegraph.db)
▼
[4. SERVE] MCP server (stdio) or web dashboard
│
▼
Claude Code / Cursor / Windsurf / Cline
receives only the relevant context, not entire filesQuick start
# Index your project
npx codegraph-ai index .
# Start MCP server (for AI agents)
npx codegraph-ai serve .
# Start web dashboard (for humans)
npx codegraph-ai dashboard .
# Query a symbol
npx codegraph-ai query getAllServers
# Run token savings benchmark
npx codegraph-ai benchmark .MCP Tools
Tool | Description |
| Full-text search for symbols (functions, classes, types) |
| Get a symbol with its dependencies and dependents |
| Get all imports and exports for a file |
| High-level stats: hub nodes, entry points, connections |
Setup with your AI agent
Claude Code
{
"mcpServers": {
"codegraph": {
"command": "npx",
"args": ["codegraph-ai", "serve", "/path/to/your/project"]
}
}
}Cursor
Add to .cursor/mcp.json:
{
"mcpServers": {
"codegraph": {
"command": "npx",
"args": ["codegraph-ai", "serve", "/path/to/your/project"]
}
}
}Windsurf
Add to MCP settings:
{
"mcpServers": {
"codegraph": {
"command": "npx",
"args": ["codegraph-ai", "serve", "/path/to/your/project"]
}
}
}Recommended CLAUDE.md instructions
Add this to your project's CLAUDE.md so your AI agent uses codegraph effectively:
## CodeGraph (read before exploring code)
This project has codegraph configured as MCP server. ALWAYS follow this flow:
1. **Before any task**: call `project_overview` to understand the structure
2. **Before searching code**: call `search` instead of grep/glob
3. **Before reading a file**: call `get_context` of the symbol you need — gives you code + dependencies + dependents without reading full files
4. **To understand a file**: call `get_file_deps` first — shows imports and exports
5. **Only read full files** when you need to edit code or codegraph context isn't enoughWhen NOT to use CodeGraph
CodeGraph is not always the right choice. Be aware of these limitations:
Stale index: If you don't use
--watchand change code, the agent gets outdated info and may make wrong decisions. Always useserve --watchor re-index after changes.Small projects: For projects with <20 files, it's faster to read files directly than making MCP calls. The overhead isn't worth it.
Editing code: When the agent needs to modify a file, it must read the full file anyway. CodeGraph helps with exploration, not editing.
Internal logic: CodeGraph only indexes exported symbols (functions, classes, types). Comments, configuration files, internal helper functions, and business logic details may not appear. Don't rely solely on codegraph for a full audit.
Rule of thumb: Use codegraph for understanding and navigating the codebase. Use file reads for editing and deep inspection.
Dashboard
Run codegraph dashboard to open an interactive visualization at http://localhost:3000:
Force-directed graph of your codebase
Click nodes to see dependencies and dependents
Search symbols with full-text search
Filter by type (functions, types, files)
Dark theme
Indexing performance
Step | Time |
Walk files | 12ms |
Parse all (82 files) | 97ms |
Store + build graph | 54ms |
Total | 163ms |
DB size: ~560 KB for 82 files / 384 symbols / 300 edges.
Supported languages
TypeScript (.ts, .tsx)
JavaScript (.js, .jsx)
Stack
tree-sitter (WASM) — parsing
better-sqlite3 — storage + FTS5
@modelcontextprotocol/sdk — MCP server
d3-force — graph visualization
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
Hosted code graph over MCP: exact callers, dependencies, and cross-repo blast radius for AI agents.
Codebase graphs, caller impact analysis, and recorded project context for AI coding agents.
shared AI-context layer for teams — persistent memory your agents search and update over MCP
Repository knowledge graph MCP server for codebase understanding and debugging.
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