io.github.bekirdag/docdex MCP Server

Docdex

Turn your repository into fast, private context that humans and AI can trust.

Docdex is a local-first indexer and search daemon for documentation and source code. It sits between your raw files and your AI assistant, providing deterministic search, code intelligence, and persistent memory without ever uploading your code to a cloud vector store.

⚡ Why Docdex?

Most AI tools rely on "grep" (fast but dumb) or hosted RAG (slow and requires uploads). Docdex runs locally, understands code structure, and gives your AI agents a persistent memory.

Problem	Typical Approach	The Docdex Solution
Finding Context	grep/rg (Noisy, literal matches)	Ranked, structured results based on intent.
Code Privacy	Hosted RAG (Requires uploading code)	Local-only indexing. Your code stays on your machine.
Siloed Search	IDE-only search bars	Shared Daemon serving CLI, HTTP, and MCP clients simultaneously.
Code Awareness	String matching	AST & Impact Graph to understand dependencies and definitions.

---

🚀 Features

• 📚 Document Indexing: Rank and summarize repo documentation instantly.
• 🧠 AST & Impact Graph: Search by function intent and track downstream dependencies (supports Rust, Python, JS/TS, Go, Java, C++, and more).
• 💾 Repo Memory: Stores project facts, decisions, and notes locally.
• 👤 Agent Memory: Remembers user preferences (e.g., "Use concise bullet points") across different repositories.
• 🗂️ Conversation Memory: Imports transcripts, keeps wake-up bundles compact, and derives repo-scoped summaries, diary entries, and working memory.
• 🕸️ Temporal Knowledge Graph: Extracts entities, edges, episodes, and code-facing links from archived conversations for timeline and neighborhood queries.
• 🧭 Wake-Up + Project Map Context: Injects compact wake-up bundles, profile truth, and cached Project map: context into OpenAI-compatible chat completions.
• 🔌 MCP Native: Auto-configures for tools like Claude Desktop, Cursor, and Windsurf.
• 🌐 Web Enrichment: Optional web search with local LLM filtering through detected local LLM services.

---

📦 Set-and-Forget Install

Install once, point your agent at Docdex, and it keeps working in the background.

1. Install via npm (Recommended)

Requires Node.js >= 18. This will download the correct binary for your OS (macOS, Linux, Windows).

npm i -g docdex

[!WARNING] Windows requirement: Docdex uses the MSVC runtime. Install the Microsoft Visual C++ Redistributable 2015-2022 (x64) before running docdex/docdexd.

• Winget: winget install --id Microsoft.VCRedist.2015+.x64
• Manual: download vc_redist.x64.exe from Microsoft: https://aka.ms/vs/17/release/vc_redist.x64.exe
• If docdexd exits with 0xC0000135, the runtime is missing.

2. Auto-Configuration

If you have any of the following clients installed, Docdex automatically configures them to use the local MCP endpoint (daemon HTTP/SSE):

Claude Desktop, Cursor, Windsurf, Cline, Roo Code, Continue, VS Code, PearAI, Void, Zed, Codex.

Note: Restart your AI client after installation.

---

🛠️ Usage Workflow

1. Index a Repository

Run this once to build the index and graph data.

docdexd index --repo /path/to/my-project

2. Start the Daemon

Start the shared server. This handles HTTP requests and MCP connections.

docdex start
# or: docdexd daemon --host 127.0.0.1 --port 28491

3. Ask Questions (CLI)

You can chat directly from the terminal.

docdexd chat --repo /path/to/my-project --query "how does auth work?"

---

🔌 Model Context Protocol (MCP)

Docdex is designed to be the "brain" for your AI agents. It exposes an MCP endpoint that agents connect to.

Architecture

flowchart LR
  Repo[Repo on disk] --> Indexer[Docdex Indexer]
  Indexer --> Daemon[Docdex Daemon]
  Daemon -->|HTTP + SSE| MCPClient[MCP Client]
  MCPClient --> Host[AI Agent / Editor]

Use the daemon HTTP/SSE endpoint. For sandboxed clients, Docdex can also serve MCP over local IPC (Unix socket or Windows named pipe), while HTTP/SSE remains the default for most MCP clients.

Manual Configuration

If you need to configure your client manually:

JSON (Claude/Cursor/Continue):

{
  "mcpServers": {
    "docdex": {
      "url": "http://127.0.0.1:28491/v1/mcp/sse"
    }
  }
}

Claude Code (CLI) JSON (~/.claude.json or project .mcp.json):

{
  "mcpServers": {
    "docdex": {
      "type": "http",
      "url": "http://127.0.0.1:28491/v1/mcp"
    }
  }
}

TOML (Codex):

[mcp_servers.docdex]
url = "http://127.0.0.1:28491/v1/mcp"
tool_timeout_sec = 300
startup_timeout_sec = 300

---

🤖 capabilities & Examples

1. AST & Impact Analysis

Don't just find the string "addressGenerator"; find the definition and what it impacts.

# Find definition
curl "http://127.0.0.1:28491/v1/ast?name=addressGenerator&pathPrefix=src"

# Track downstream impact (what breaks if I change this?)
curl "http://127.0.0.1:28491/v1/graph/impact?file=src/app.ts&maxDepth=3"

2. Memory System

Docdex allows you to store "facts" that retrieval helps recall later.

Repo Memory (Project specific):

# Teach the repo a fact
docdexd memory-store --repo . --text "Payments retry up to 3 times with backoff."

# Recall it later
docdexd memory-recall --repo . --query "payments retry policy"

Agent Memory (User preference):

# Set a style preference
docdexd profile add --agent-id "default" --category style --content "Use concise bullet points."

3. Conversation Memory

Conversation memory is repo-scoped by default and optional. Repo-less sessions must use an explicit conversation namespace so they never silently reuse a repo archive. The subsystem imports transcripts, stores episodic summaries and working memory, derives diary entries and temporal KG facts into knowledge.db, and keeps recall under a strict wake-up budget.

The CLI archive, diary, and hook commands are HTTP-backed wrappers, so start docdex start or docdexd daemon first.

# Archive and inspect transcripts
docdexd conversations import --repo . ./session.txt --format plain_text --agent-id codex
docdexd conversations list --repo . --agent-id codex
docdexd conversations search --repo . "timeline_index"
docdexd conversations read --repo . <session_id>

# Import into an explicit global conversation namespace instead of a repo archive
docdexd conversations import --conversation-namespace shared-team ./session.txt --format plain_text --agent-id codex
docdexd conversations search --conversation-namespace shared-team "timeline_index"

# Keep agent diary notes alongside imported sessions
docdexd diary write --repo . --agent-id codex "Wake-up rollout validated against knowledge.db timeline output."
docdexd diary read --repo . --agent-id codex

# Trigger durable summarization from an external transcript
docdexd hook conversation --repo . \
  --action session_close_summarization \
  --source codex \
  --agent-id codex \
  --transcript ./session.txt \
  --format plain_text \
  --wait-for-processing

# Build a compact wake-up bundle over recent context
curl -X POST http://127.0.0.1:28491/v1/wakeup \
  -H "Content-Type: application/json" \
  -d '{"agent_id":"codex","query":"timeline_index","max_tokens":96}'

# Address the same archive over HTTP without repo_id
curl -X POST http://127.0.0.1:28491/v1/wakeup \
  -H "Content-Type: application/json" \
  -H "x-docdex-conversation-namespace: shared-team" \
  -d '{"agent_id":"codex","query":"timeline_index","max_tokens":96}'

# Explore derived repo-scoped knowledge facts and provenance
curl "http://127.0.0.1:28491/v1/kg/query?q=knowledge.db&limit=10"
curl "http://127.0.0.1:28491/v1/kg/search/nodes?q=knowledge&limit=10"
curl "http://127.0.0.1:28491/v1/kg/neighborhood?entity=knowledge.db&limit=10"
curl "http://127.0.0.1:28491/v1/kg/timeline?entity=knowledge.db&limit=10"

# Chat with wake-up + project-map context and inspect reasoning trace metadata
curl -X POST http://127.0.0.1:28491/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "fake-model",
    "messages": [{"role": "user", "content": "What changed around knowledge.db?"}],
    "docdex": {
      "agent_id": "codex",
      "limit": 6,
      "include_libs": true,
      "dag_session_id": "session-123"
    }
  }'

4. Local LLM Services

Docdex detects supported local LLM services before it suggests installing anything. It can reuse Ollama, vLLM, llama.cpp-compatible OpenAI endpoints, LM Studio, LocalAI, SGLang, TGI-compatible deployments, and healthy local mcoda agents when they are already present. Ollama remains the recommended fallback because it is the easiest guided setup path.

• Setup: Run docdex setup for an interactive wizard that lists detected services, models, embedding candidates, and local delegation agents.
• Inspect: Run docdexd llm detect --json or docdexd llm diagnostics --json to see why a service/model was selected, skipped, or marked unhealthy.
• Manual Ollama fallback: If no usable service is installed, pull the fallback embedding model with ollama pull nomic-embed-text.
• Custom Ollama URL:

DOCDEX_OLLAMA_BASE_URL=http://127.0.0.1:11434 docdex start --host 127.0.0.1 --port 28491

---

⚙️ Configuration & HTTP API

Docdex runs as a local daemon serving:

• CLI Commands: docdexd chat
• HTTP API: /search, /v1/capabilities, /v1/search/rerank, /v1/search/batch, /v1/chat/completions, /v1/ast, /v1/graph/impact, /v1/conversations/*, /v1/diary/*, /v1/hooks/conversation, /v1/wakeup, /v1/kg/*
• MCP Endpoints: /v1/mcp and /v1/mcp/sse
• Capability Negotiation Tools: docdex_capabilities, docdex_rerank, docdex_batch_search, docdex_conversation_*, docdex_diary_*, docdex_conversation_hook, docdex_wakeup, docdex_kg_*

Multi-Repo Setup

Run a single daemon and mount additional repos on demand.

docdex start --port 28491

# Mount repos and capture repo_id values
curl -X POST "http://127.0.0.1:28491/v1/initialize" \
  -H "Content-Type: application/json" \
  -d '{"rootUri":"file:///path/to/repo-a"}'

curl -X POST "http://127.0.0.1:28491/v1/initialize" \
  -H "Content-Type: application/json" \
  -d '{"rootUri":"file:///path/to/repo-b"}'

Notes:

• When more than one repo is mounted (or the daemon starts without a default repo), include x-docdex-repo-id: <sha256> on HTTP requests.
• MCP sessions bind to the repo provided in initialize.rootUri and reuse that repo automatically.

Security

• Secure Mode: By default, Docdex enforces TLS on non-loopback binds.
• Loopback: 127.0.0.1 is accessible without TLS for local agents.
• To expose to a network (use with caution), use --expose and --auth-token.

---

📚 Learn More

• Detailed Usage: docs/usage.md
• API Reference: docs/http_api.md
• MCP Specs: docs/mcp/errors.md

<a href="https://glama.ai/mcp/servers/@bekirdag/docdex"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@bekirdag/docdex/badge" /> </a>