RIG MCP Tools

A Model Context Protocol (MCP) server providing intelligent code analysis, graph-based architecture insights, and file operations for AI assistants.

Overview

RIG MCP Tools combines three layers of intelligence:

• Static analysis — A Repository Intelligence Graph (RIG) built from AST parsing (ts-morph, tree-sitter) stored in SQLite, enabling graph queries with zero LLM cost.
• Semantic search — Embedding-based symbol retrieval using a local model (nomic-embed-text or compatible). Embeddings are cached in SQLite after the first run. Returns precise code snippets instead of whole files — minimizes token usage.
• LLM-powered tools — A subset of tools that call a configurable OpenAI-compatible API for natural language reasoning over code.

Installation

From npm

npx rig-mcp-tools

From source

git clone <repository-url>
cd rig-mcp-tools
npm install
npm run build

Docker

docker build -t rig-mcp-tools .
docker run rig-mcp-tools

Configuration

MCP client (Claude Desktop, Cursor, etc.)

{
  "mcpServers": {
    "rig-tools": {
      "command": "node",
      "args": ["/path/to/dist/index.js"],
      "env": {
        "WORKSPACE_PATH": "/your/project",
        "GLM_API_URL": "http://localhost:1234/v1/chat/completions",
        "GLM_MODEL": "qwen2.5-coder-7b-instruct-mlx@8bit",
        "EMBEDDING_API_URL": "http://localhost:1234/v1/embeddings",
        "EMBEDDING_MODEL": "text-embedding-nomic-embed-text-v1.5"
      }
    }
  }
}

Environment variables

| Variable | Default | Description | |---|---|---| | WORKSPACE_PATH | /workspace | Root workspace path | | GLM_API_URL | http://localhost:1234/v1/chat/completions | LLM API endpoint (OpenAI-compatible) | | GLM_MODEL | qwen2.5-coder-7b-instruct-mlx@8bit | Model for LLM-powered tools | | EMBEDDING_API_URL | http://localhost:1234/v1/embeddings | Embeddings API endpoint | | EMBEDDING_MODEL | text-embedding-nomic-embed-text-v1.5 | Model for semantic search |

LLM and embedding tools are optional — all static analysis tools work without any API.

Available Tools

RIG — Graph Analysis

These tools index the repository into a SQLite graph via AST parsing and query it without calling any LLM.

get_smart_context

Retrieve the most relevant files and symbols for a query using graph centrality and keyword scoring.

{ "rootPath": "/project", "text": "authentication flow" }

get_architectural_metrics

Executive summary of repository architecture: core hubs, entry points, and stable foundations ranked by graph centrality.

{ "rootPath": "/project" }

graph_analyzer

Component-level complexity analysis with hotspot detection and refactor recommendations.

{ "rootPath": "/project" }

generate_call_graph

Generate call graphs or dependency diagrams between components, files, or symbols.

{
  "rootPath": "/project",
  "level": "component",
  "format": "mermaid",
  "maxDepth": 5
}

level: "component" | "file" | "symbol"
format: "mermaid" | "dot" | "json"

generate_diagram

Generate C4 architecture diagrams, sequence diagrams, call graphs, or dependency visualizations from the RIG.

{
  "rootPath": "/project",
  "type": "c4-container",
  "format": "mermaid",
  "focus": "auth",
  "maxDepth": 3,
  "style": "default"
}

type: "c4-context" | "c4-container" | "c4-component" | "sequence" | "call-graph" | "dependency-graph"
format: "mermaid" | "plantuml" | "dot"
style: "default" | "compact" | "detailed"

extract_method

Surgically extract a function or class from a source file to a target file using RIG symbol coordinates.

{
  "rootPath": "/project",
  "sourceFile": "src/utils/helpers.ts",
  "symbolName": "formatDate",
  "targetFile": "src/utils/date.ts"
}

File Operations

Pure filesystem tools, no graph or LLM required.

read_files

Read content of up to 10 files in a single call.

{ "files": ["src/index.ts", "src/config.ts"] }

write_code_unit

Write or overwrite a file with specific content. Creates parent directories as needed.

{ "path": "src/utils/new-file.ts", "content": "export const foo = 1;" }

ls_tree

List directory structure as an ASCII tree.

{ "path": "/project/src", "maxDepth": 3 }

search_code

Search text or regex patterns recursively across the codebase.

{ "path": "/project/src", "pattern": "useEffect", "useRegex": false }

inspect_symbols

Extract class and function signatures from a file using AST analysis (ts-morph).

{ "file": "src/tools/index.ts" }

run_shell_task

Execute allowed shell commands.

{ "command": "npm run build", "timeout": 60000 }

Allowed prefixes: npm test, npm run, npm list, npx vitest, npx tsc, npx eslint, node --version, tsc, git status, git diff, git log, git show, git blame, ls, pwd, cat, wc.

Quality Analysis

Static analysis tools, no LLM required.

detect_patterns

Detect anti-patterns, code smells, and security issues using Babel AST analysis.

{ "sourceCode": "...", "filePath": "src/auth/login.ts" }

suggest_refactor

Detect refactoring opportunities: long functions, deep nesting, magic numbers, duplicate code, and missing type annotations.

{
  "file_path": "src/services/user.ts",
  "max_suggestions": 10,
  "min_priority": 3,
  "include_diff": true
}

Either file_path or code_snippet must be provided.

analyze_dependencies

Build a lightweight dependency graph of TypeScript files via import analysis. Returns nodes, circular dependencies, and DOT format for Graphviz.

{ "rootPath": "/project/src" }

Embedding-Powered

Requires EMBEDDING_API_URL and EMBEDDING_MODEL. Embeddings are generated once per symbol and cached in .rig/index.db — subsequent queries only embed the query string.

search_semantic

Semantic symbol search using vector similarity. Returns the most relevant functions and classes with their code snippets. Use this before read_files to avoid loading entire files into context. Embeddings are generated once per symbol and cached in .rig/index.db.

{
  "repoPath": "/project",
  "query": "how is authentication handled",
  "maxResults": 5,
  "threshold": 0.3
}

LLM-Powered

Requires GLM_API_URL and GLM_MODEL.

analyze_logic

Ask a natural language question about a piece of code. Uses the configured LLM to reason about behavior, intent, or logic.

{
  "filePath": "/project/src/auth/login.ts",
  "question": "What edge cases does this miss?"
}

Prefer filePath over code to avoid passing file contents through context.

smart_summarize

Generate an intelligent summary of a code file including imports, exports, purpose, and key dependencies.

{ "filePath": "/project/src/services/user.ts", "maxLength": 200 }

generate_unit_tests

Generate vitest unit tests for a specific function or class. Uses the RIG index to extract only the method body (not the whole file) — token-efficient. Covers happy path, edge cases, and error cases.

{
  "repoPath": "/project",
  "symbolName": "createUser",
  "filePath": "src/services/user.ts"
}

filePath is required only if the symbol exists in multiple files.

investigate_ts_fix

Run tsc --noEmit and use the LLM to explain and suggest minimal fixes for each TypeScript error. Token-efficient: only passes the snippet around the error line (±8 lines), not the whole file.

{
  "repoPath": "/project",
  "filePath": "src/services/user.ts",
  "maxErrors": 5
}

filePath and maxErrors are optional. Omit filePath to investigate all errors across the repo.

Architecture

src/
├── cli/           # rig-indexer CLI (pre-index a repo into .rig/index.db)
├── graph/         # RIG graph engine (indexer, parsers, SQLite storage, types)
├── security/      # Path validation and safe extension checks
└── tools/         # MCP tool implementations (19 tools)

Pre-indexing a repository

For large codebases, pre-index before using RIG tools:

npx tsx src/cli/index.ts /path/to/project --db /path/to/project/.rig/index.db

Options: --max-files <n>, --include-tests, --json

Development

npm run build   # Compile TypeScript
npm run dev     # Run in development mode
npm test        # Run test suite
npm run clean   # Clean build artifacts

License

MIT