MCPInstall

codebase-memory-mcp-intelligence

High-performance code intelligence MCP server that indexes codebases into knowledge graphs for structural queries, call traces, and architecture analysis

Installs
505
Last 24h
—
Trending rank
—
Security
Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/reason-machines/mcp-skills --skill codebase-memory-mcp-intelligence
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads codebase-memory-mcp-intelligence automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

codebase-memory-mcp Intelligence

Skill by ara.so — MCP Skills collection.

Overview

codebase-memory-mcp is a high-performance code intelligence MCP server that indexes codebases into persistent knowledge graphs. It parses 155 languages using tree-sitter ASTs, resolves call graphs, detects HTTP routes, and enables sub-millisecond structural queries. The Linux kernel (28M LOC) indexes in 3 minutes; average repos in milliseconds.

Key capabilities:

  • Graph-based code intelligence: Functions, classes, calls, imports, inheritance as queryable nodes/edges
  • Structural search: Find symbols by pattern, trace call chains, detect dead code
  • Architecture analysis: Community detection, hotspots, layering, cross-service HTTP linking
  • Semantic search: Vector search with bundled embeddings (no API key required)
  • 155 languages: Tree-sitter grammars compiled into a single static binary
  • 14 MCP tools: All accessible via natural language in any compatible agent

Installation

Quick Install (macOS/Linux)

# Standard version
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash

# With graph visualization UI
curl -fsSL https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.sh | bash -s -- --ui

Windows (PowerShell)

Invoke-WebRequest -Uri https://raw.githubusercontent.com/DeusData/codebase-memory-mcp/main/install.ps1 -OutFile install.ps1
.\install.ps1

Manual Installation

Download from releases:

# Extract and run installer
tar xzf codebase-memory-mcp-*.tar.gz
./install.sh

The installer auto-detects Claude Code, Cursor, Codex CLI, Gemini CLI, Zed, OpenCode, Aider, VS Code, and other agents, configuring MCP entries automatically.

Verify Installation

codebase-memory-mcp --version

Restart your coding agent after installation.

Core Usage Patterns

Indexing a Codebase

Via agent (recommended):

User: Index this project

The MCP server will call index_repository with the current working directory.

Via CLI:

codebase-memory-mcp index /path/to/repo

Enable auto-indexing:

codebase-memory-mcp config set auto_index true
codebase-memory-mcp config set auto_index_limit 50000

Auto-index runs on MCP session start for new projects and registers existing projects with the background file watcher.

Architecture Overview

Get a complete architectural summary in one call:

User: Show me the architecture of this codebase

This invokes get_architecture which returns:

  • Languages and package structure
  • Entry points (main functions, HTTP handlers)
  • HTTP/gRPC/GraphQL routes
  • Hotspot functions (high in/out degree)
  • Module boundaries via Louvain clustering
  • Layering analysis (presentation/business/data)

Example response structure:

{
  "languages": ["TypeScript", "Python", "Go"],
  "packages": ["src/api", "src/services", "src/models"],
  "entry_points": [
    {"name": "main", "file": "src/index.ts", "type": "Function"}
  ],
  "routes": [
    {"path": "/api/users", "method": "GET", "handler": "getUsers", "file": "src/api/users.ts"}
  ],
  "hotspots": [
    {"name": "processRequest", "in_degree": 15, "out_degree": 8, "file": "src/services/request.ts"}
  ],
  "boundaries": [
    {"module": "Community_0", "members": ["src/api/*"], "internal_edges": 45, "external_edges": 12}
  ]
}

Searching the Graph

Find symbols by name pattern:

User: Find all functions with "Handler" in the name

Uses search_graph:

{
  "name_pattern": ".*Handler.*",
  "labels": ["Function"]
}

Find high-degree nodes:

User: Show me the most called functions
{
  "labels": ["Function"],
  "min_in_degree": 5,
  "limit": 20
}

Scope to specific files:

User: Find all classes in the auth module
{
  "labels": ["Class"],
  "file_pattern": ".*/auth/.*"
}

Call Tracing

Find all callers:

User: What calls the authenticate function?

Uses get_callers:

{
  "symbol_name": "authenticate",
  "max_depth": 3
}

Trace call chain:

User: Trace the execution path from main to database functions

Uses trace_symbol:

{
  "symbol_name": "main",
  "direction": "outgoing",
  "max_depth": 5,
  "edge_types": ["CALLS"]
}

Semantic Search

Find related code by concept:

User: Find authentication-related functions

Uses semantic_query:

{
  "query": "authentication jwt token validation",
  "limit": 10
}

Semantic search uses bundled Nomic embeddings (no API key required) with 11-signal scoring:

  • TF-IDF relevance
  • AST structural similarity
  • API signature matching
  • Data flow patterns
  • MinHash near-clone detection

Cypher-Like Queries

Advanced graph queries:

User: Run a Cypher query to find all functions that call database operations

Uses cypher_query:

MATCH (f:Function)-[:CALLS]->(db:Function)
WHERE db.name CONTAINS 'query' OR db.name CONTAINS 'exec'
RETURN f.name, f.file, COUNT(db) as db_calls
ORDER BY db_calls DESC
LIMIT 20

Find inheritance hierarchies:

MATCH (c:Class)-[:INHERITS*1..3]->(base:Class)
WHERE base.name = 'BaseModel'
RETURN c.name, c.file

HTTP route to implementation:

MATCH (r:Route)-[:HANDLED_BY]->(h:Function)-[:CALLS]->(s:Function)
WHERE r.path CONTAINS '/api/users'
RETURN r.path, h.name, COLLECT(s.name) as services

Dead Code Detection

User: Find unused functions

Uses find_dead_code:

{
  "entry_points": ["main", "handler"],
  "min_depth": 2
}

Returns functions with zero incoming CALLS edges, excluding recognized entry points (main, init, handlers, tests).

Change Impact Analysis

Detect impact of uncommitted changes:

User: What's affected by my recent changes?

Uses detect_changes:

{
  "include_tests": true
}

Returns:

  • Changed symbols with confidence scores
  • Affected downstream symbols (via call graph)
  • Risk classification (CRITICAL/HIGH/MEDIUM/LOW)
  • Test coverage mapping

Example response:

{
  "changed_symbols": [
    {
      "name": "authenticate",
      "type": "Function",
      "file": "src/auth.ts",
      "confidence": 0.95,
      "change_type": "modified"
    }
  ],
  "affected_symbols": [
    {
      "name": "loginHandler",
      "type": "Function",
      "file": "src/handlers/login.ts",
      "distance": 1,
      "risk": "HIGH"
    }
  ]
}

Architecture Decision Records (ADR)

Create ADR:

User: Document our decision to use Redis for session storage

Uses manage_adr:

{
  "action": "create",
  "title": "Use Redis for Session Storage",
  "status": "accepted",
  "context": "Need fast, distributed session management",
  "decision": "Adopt Redis with session TTL and clustering",
  "consequences": "Requires Redis infrastructure; enables horizontal scaling"
}

List ADRs:

User: Show all architecture decisions
{
  "action": "list"
}

Update ADR:

{
  "action": "update",
  "id": "ADR-001",
  "status": "superseded"
}

Cross-Service Analysis

Find HTTP call sites:

User: Show all external API calls

Uses search_graph with HTTP edge filters:

{
  "edge_types": ["HTTP_CALLS"],
  "labels": ["Function"]
}

Trace cross-service dependencies:

MATCH (f:Function)-[:HTTP_CALLS]->(r:Route)
RETURN f.name, f.file, r.path, r.method

Configuration

View Current Config

codebase-memory-mcp config list

Key Settings

# Auto-index on session start
codebase-memory-mcp config set auto_index true

# File limit for auto-indexing
codebase-memory-mcp config set auto_index_limit 50000

# Enable background file watcher
codebase-memory-mcp config set watch true

# Embedding model (default: nomic-embed-code)
codebase-memory-mcp config set embedding_model nomic-embed-code

# Graph visualization port
codebase-memory-mcp config set ui_port 9749

Ignore Patterns

Create .cbmignore in your repo root (gitignore syntax):

node_modules/
*.test.ts
dist/
build/
*.min.js
vendor/

Default ignores: node_modules, .git, dist, build, vendor, .venv, __pycache__

Advanced Features

Graph Visualization UI

Launch with UI enabled:

codebase-memory-mcp --ui=true --port=9749

Open http://localhost:9749 for 3D interactive graph exploration.

Features:

  • Force-directed layout with community coloring
  • Filter by label, file, package
  • Click nodes to see properties and edges
  • Multi-galaxy layout for cross-repo graphs

Team-Shared Graph Artifact

Commit compressed graph to avoid reindexing:

# After indexing, compress the graph
codebase-memory-mcp export --output=.codebase-memory/graph.db.zst

# Commit to repo
git add .codebase-memory/graph.db.zst
git commit -m "Add codebase graph snapshot"

Teammates run:

codebase-memory-mcp import --input=.codebase-memory/graph.db.zst

CLI Mode

Use without MCP for scripting:

# Search
codebase-memory-mcp cli search_graph '{"name_pattern": ".*Service$", "labels": ["Class"]}'

# Cypher query
codebase-memory-mcp cli cypher_query 'MATCH (f:Function) WHERE f.name = "main" RETURN f'

# Get architecture
codebase-memory-mcp cli get_architecture '{}'

# Semantic search
codebase-memory-mcp cli semantic_query '{"query": "database connection pooling", "limit": 5}'

Language-Specific Features

Go:

  • Package resolution via go.mod
  • Interface implementation detection
  • Struct method resolution

TypeScript/JavaScript:

  • JSX component tracing
  • Type inference from JSDoc
  • Import alias resolution
  • Decorator detection

Python:

  • Class hierarchy via INHERITS
  • Decorator-based route detection (Flask, FastAPI)
  • Import resolution via pyproject.toml, setup.py

C/C++:

  • Header/implementation linking
  • Preprocessor-aware parsing
  • Template instantiation tracking

Infrastructure-as-Code

Kubernetes manifests:

User: Show all Kubernetes resources
{
  "labels": ["Resource"],
  "properties": {"kind": "Deployment"}
}

Kustomize overlays:

MATCH (overlay:Module)-[:IMPORTS]->(base:Resource)
WHERE overlay.type = 'kustomize'
RETURN overlay.name, COLLECT(base.name) as resources

Dockerfile analysis:

User: Find all services using Node 18
{
  "labels": ["Dockerfile"],
  "code_pattern": "FROM node:18"
}

Troubleshooting

Indexing Issues

Large repos timing out:

# Increase memory limit (default: 8GB)
codebase-memory-mcp config set max_memory_gb 16

# Skip large files
codebase-memory-mcp config set max_file_size_mb 5

Missing symbols:

  • Check .cbmignore — ensure source files aren't excluded
  • Verify language support: codebase-memory-mcp --languages
  • Re-index: codebase-memory-mcp index --force /path/to/repo

Slow queries:

# Rebuild indexes
codebase-memory-mcp cli vacuum

# Check database size
ls -lh ~/.cache/codebase-memory-mcp/graph.db

MCP Connection Issues

Agent can't find server:

  1. Verify installation: which codebase-memory-mcp
  2. Check agent config: codebase-memory-mcp config verify
  3. Restart agent completely
  4. Check logs: ~/.cache/codebase-memory-mcp/logs/

Tools not appearing:

# Reinstall agent configs
codebase-memory-mcp install --force

# Verify MCP registration
codebase-memory-mcp config show-mcp-config

Performance

High memory usage during indexing:

  • Normal for large repos (Linux kernel uses ~6GB peak)
  • Memory released after indexing completes
  • Use --max-memory-gb flag to cap usage

Slow semantic search:

# Rebuild embeddings index
codebase-memory-mcp cli rebuild_embeddings

# Reduce result limit
codebase-memory-mcp config set semantic_limit 10

File Watcher

Changes not detected:

# Check watcher status
codebase-memory-mcp status

# Restart watcher
codebase-memory-mcp watch restart

# Increase file watch limit (Linux)
echo fs.inotify.max_user_watches=524288 | sudo tee -a /etc/sysctl.conf
sudo sysctl -p

Common Workflows

Onboarding to New Codebase

1. "Index this project"
2. "Show me the architecture"
3. "What are the entry points?"
4. "Find the main HTTP routes"
5. "Show me the most important functions" (hotspots)

Refactoring

1. "Find all callers of [function]"
2. "What's affected by my changes?" (after editing)
3. "Show me the call chain from [entry] to [target]"
4. "Find dead code"

Debugging

1. "Trace execution from [function]"
2. "What calls this endpoint?" (for HTTP routes)
3. "Find similar functions to [name]"
4. "Show data flow through [function]"

Cross-Service Analysis

1. "Find all external API calls"
2. "Show services that depend on [service]"
3. "Map HTTP routes to implementations"
4. "Find pub-sub channels across repos"

MCP Tools Reference

ToolPurpose
index_repositoryIndex codebase into knowledge graph
search_graphStructural search by pattern, label, degree
search_codeGraph-augmented grep
semantic_queryVector search with bundled embeddings
get_architectureFull architectural summary
get_callersFind all callers of a symbol
trace_symbolTrace call chains (in/out)
cypher_queryAdvanced graph queries
find_dead_codeDetect unused functions
detect_changesGit diff impact analysis
manage_adrArchitecture Decision Records
get_contextContext for specific symbols
list_routesAll HTTP/gRPC/GraphQL routes
analyze_dependenciesCross-service dependency graph

Updates

# Check for updates
codebase-memory-mcp update

# Auto-update check on startup (default: enabled)
codebase-memory-mcp config set check_updates true

The server notifies on first tool call if a new release is available.

Uninstall

# Remove all agent configs and binary
codebase-memory-mcp uninstall

# Keep database (default)
# Or remove database too:
rm -rf ~/.cache/codebase-memory-mcp/

FAQ

What does the codebase-memory-mcp-intelligence skill do?

High-performance code intelligence MCP server that indexes codebases into knowledge graphs for structural queries, call traces, and architecture analysis

How do I install codebase-memory-mcp-intelligence?

Run npx skills add https://github.com/reason-machines/mcp-skills --skill codebase-memory-mcp-intelligence in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/codebase-memory-mcp-intelligence/ (personal) or .claude/skills/codebase-memory-mcp-intelligence/ (project).

Which AI agents can use codebase-memory-mcp-intelligence?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is codebase-memory-mcp-intelligence safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains codebase-memory-mcp-intelligence?

It is published by reason-machines in reason-machines/mcp-skills. It has 505 recorded installs.

More from reason-machines/mcp-skills

View all →

playwright-mcp-server

reason-machines/mcp-skills

Open the skill to read its SKILL.md instructions.

AI & Agents272 installs

tradingview-mcp-assistant

reason-machines/mcp-skills

Open the skill to read its SKILL.md instructions.

AI & Agents256 installs

lanhu-mcp-collaboration

reason-machines/mcp-skills

Open the skill to read its SKILL.md instructions.

AI & Agents245 installs

github-mcp-server

reason-machines/mcp-skills

Open the skill to read its SKILL.md instructions.

AI & Agents237 installs

google-meta-ads-ga4-mcp

reason-machines/mcp-skills

Open the skill to read its SKILL.md instructions.

AI & Agents233 installs

chrome-devtools-mcp-automation

reason-machines/mcp-skills

Open the skill to read its SKILL.md instructions.

AI & Agents233 installs

Popular ai & agents skills

View category →

google-agents-cli-publish

google/agents-cli

This skill should be used when the user wants to "publish an agent", "publish my ADK agent", "register an agent with Gemini", "publish to Gemini" (formerly Gemini Enterprise), or needs guidance on the agents-cli publish gemini command. Also use when the user wants to "manage agents in Agent Registry", "list/update/delete registered agents", or "register an MCP server". Covers ADK vs A2A registration modes, programmatic and interactive usage, flag reference, auto-detection from deployment metadata, Agent Registry fleet management, and troubleshooting. Part of the agents-cli skills suite. Do NOT use for deployment (use google-agents-cli-deploy).

AI & Agents513.9K installs

google-agents-cli-eval

google/agents-cli

This skill should be used when the user wants to "run an evaluation", "evaluate my agent", "evaluate my ADK agent", "write an eval dataset", "analyze eval failures", "compare eval results", "optimize agent", or needs guidance on the Gemini platform eval methodology and the Quality Flywheel. Covers eval metrics, dataset schema, LLM-as-judge scoring, and common failure causes. Applies to any agents-cli project, whatever framework the agent is written in. Do NOT use for agent API code patterns (ADK: use google-agents-cli-adk-code), deployment (use google-agents-cli-deploy), or project scaffolding (use google-agents-cli-scaffold).

AI & Agents513.5K installs

skill-creator

anthropics/skills

Create new skills, modify and improve existing skills, and measure skill performance. Use when users want to create a skill from scratch, edit, or optimize an existing skill, run evals to test a skill, benchmark skill performance with variance analysis, or optimize a skill's description for better triggering accuracy.

OfficialAI & Agents416.4K installs

codex-pet

prime-skills/runcomfy-agent-skills

Codex Pet generator on RunComfy. Build a Codex-compatible Codex Pet spritesheet.webp + pet.json from a single reference image, drop it into ${CODEXHOME:-$HOME/.codex}/pets/ / and Codex picks it up as a custom Codex Pet next to the 8 built-ins. This skill produces the exact Codex Pet atlas Codex expects (1536x1872 PNG/WebP, 8 cols x 9 rows, 192x208 cells, 9 animation states — idle, running-right, running-left, waving, jumping, failed, waiting, running, review). Calls OpenAI GPT Image 2 edit ONCE via the local RunComfy CLI as runcomfy run openai/gpt-image-2/edit to produce a canonical Codex Pet pose, then assembles all 9 animation rows programmatically with ImageMagick micro-transforms — no Codex Pro, no $imagegen, no OPENAIAPIKEY required, only RUNCOMFYTOKEN. Triggers on "codex pet", "create codex pet", "make codex pet", "hatch codex pet", "/hatch image", "desktop pet codex", "codex pets", "spritesheet.webp", or any explicit ask to build a custom pet for OpenAI Codex.

AI & Agents402.1K installs

full-output-enforcement

leonxlnx/taste-skill

Overrides default LLM truncation behavior. Enforces complete code generation, bans placeholder patterns, and handles token-limit splits cleanly. Apply to any task requiring exhaustive, unabridged output.

AI & Agents401K installs

executing-plans

obra/superpowers

Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available

AI & Agents245.7K installs