Swarms MCP Documentation Server
An Agent Framework Documentation server that enables AI agents to efficiently retrieve information from documentation databases using hybrid semantic and keyword search for seamless agent integration.
README
🐝 Swarms MCP Documentation Server
<p align="center"> <img src="https://img.shields.io/badge/Windsurf_Ready-✅-orange" alt="IDE Ready"> <img src="https://img.shields.io/badge/Error_Tolerant-✅-green" alt="Error Tolerant"> <img src="https://img.shields.io/badge/Dynamic_MD_Loader-✅-blue" alt="Dynamic MD Loader"> <img src="https://img.shields.io/badge/Healthcheck_Tool-✅-success" alt="Healthcheck Tool"> <img src="https://img.shields.io/badge/Smart_Load_Logs-✅-purple" alt="Smart Load Logs"> </p>
📖 Description
This program is an Agent Framework Documentation MCP Server built on FastMCP, designed to enable AI agents to efficiently retrieve information from your documentation database. It combines hybrid semantic (vector) and keyword (BM25) search, chunked indexing, and a robust FastMCP tools API for seamless agent integration.
Key Capabilities:
- Efficient, chunk-level retrieval using both semantic and keyword search
- Agents can query, list, and retrieve documentation using FastMCP tools
- Local-first, low-latency design (all data indexed and queried locally)
- Automatic reindexing on file changes
- Modular: add any repos to
corpora/, support for all major filetypes - Extensible: add new tools, retrievers, or corpora as needed
Main modules:
embed_documents.py→ Loads, chunks, and embeds documentsswarms_server.py→ Brings up the MCP server and FastMCP tools
🌟 Key Features
- Hybrid Retriever 🔍: Combines semantic and keyword search.
- Dynamic Markdown Handling 📄: Smart loader based on file size.
- Specialized Loaders ⚙️:
.py,.ipynb,.md,.txt,.yaml,.yml. - Chunk and File Summaries 📈: Displays chunk counts along with file counts.
- Live Watchdog 🔥: Instantly responds to any changes in
corpora/. - User Confirmation for Costs ✅: Confirms before expensive embeddings.
- Healthcheck Endpoint 🚑: Ensure server is ready for use.
- Local-First 🗂️: All repos indexed locally without external dependencies.
- Safe Deletion Helper 🔥: Auto-delete broken/mismatched indexes.
🏗️ Version History
| Version | Date | Highlights |
|---|---|---|
| 2.2 | 2025‑04‑25 | Split embed/load from server; full chunk counting in loading summaries |
| 1.0 | 2025‑04‑25 | Dynamic Markdown loader, color logs, Healthcheck tool |
| 0.7 | 2025‑04‑25 | Specialized file loaders for .py, .ipynb, .md |
| 0.5 | 2025‑04‑10 | OpenAI large model embeddings, extended MCP tools |
| 0.1 | 2025‑04‑10 | Initial version with generic loaders |
📚 Managing Your Corpora (Local Repos)
Because Swarms and other frameworks are very large, full corpora are not pushed to GitHub.
Instead, you clone them manually under corpora/:
# Inside your project folder:
cd corpora/
# Clone useful frameworks:
git clone https://github.com/SwarmsAI/Swarms
git clone https://github.com/SwarmsAI/Swarms-Examples
git clone https://github.com/microsoft/autogen
git clone https://github.com/langchain-ai/langgraph
git clone https://github.com/openai/openai-agent-sdk
✅ Notes:
- Add any repo — public, private, custom.
- Build your own custom AI knowledge base locally.
- Large repos (>500MB) are fine; all indexing is local.
🚀 Quick Start
# 1. Activate virtual environment
venv\Scripts\Activate.ps1
# 2. Install all dependencies
pip install -r requirements.txt
# 3. Configure OpenAI API Key
echo OPENAI_API_KEY=sk-... > .env
# 4. (Load and embed documents
python embed_documents.py
# 5. Start MCP server
python swarms_server.py
# If no index is found, the server will prompt you to embed documents automatically.
⚙️ Configuration
- Corpus: Drop repos inside
corpora/ - Environment Variables:
.envmust containOPENAI_API_KEY
- Index File Support:
- Both
chroma-collections.parquetandchroma.sqlite3are supported..parquetis preferred if both exist.
- Both
- Auto-Embedding:
- If no index is found, the server will prompt you to embed and index your documents automatically.
- Optional:
- Disable Chroma compaction if you prefer:
setx CHROMA_COMPACTION_SERVICE__COMPACTOR__DISABLED_COLLECTIONS "swarms_docs"
- Disable Chroma compaction if you prefer:
- Command-Line Flags:
--reindex→ trigger a refresh reindex during server run.
🔄 File Watching & Auto Reindexing
The MCP Server watches corpora/ for any file changes:
- Any modification, creation, or deletion triggers a live reindex.
- No need to restart the server.
🛠️ Available FastMCP Tools
| Tool | Description |
|---|---|
swarm_docs.search |
Search relevant documentation chunks |
swarm_docs.list_files |
List all indexed files |
swarm_docs.get_chunk |
Get a specific chunk by path and index |
swarm_docs.reindex |
Force reindex (full or incremental) |
swarm_docs.healthcheck |
Check MCP Server status |
❓ Troubleshooting
- Q: I get 'No valid existing index found' when starting the server.
- A: The server will now prompt you to embed and index documents. Accept the prompt to proceed, or run
python embed_documents.pymanually first.
- A: The server will now prompt you to embed and index documents. Accept the prompt to proceed, or run
- Q: Which index file is used?
- A: The server will use
chroma-collections.parquetif available, otherwisechroma.sqlite3.
- A: The server will use
- Q: I want to force a reindex.
- A: Run
python swarms_server.py --reindexor use theswarm_docs.reindextool.
- A: Run
📋 Example Usage
# Search the documentation
result = swarm_docs.search("How do I load a notebook?")
print(result)
# List all available files
files = swarm_docs.list_files()
print(files)
# Get a specific document chunk
chunk = swarm_docs.get_chunk(path="examples/agent.py", chunk_idx=2)
print(chunk["content"])
🧰 Extending & Rebuilding
-
Add new docs → drop into
corpora/, then:python swarms_server.py --reindex -
Schema changes → (e.g. different metadata structure):
python swarms_server.py --reindex --full -
Add new repo → Drop folder under
corpora/, reindex. -
Recommended for mostly read-only repos:
setx CHROMA_COMPACTION_SERVICE__COMPACTOR__DISABLED_COLLECTIONS "swarms_docs"
🔗 IDE Integration
Plug directly into Windsurf Cascade:
"swarms": {
"command": "C:/…/Swarms/venv/Scripts/python.exe",
"args": ["swarms_server.py"]
}
Then you can access swarm_docs.* tools from Cascade automations.
📦 Requirements
💡 Python 3.11 Environment Required
Create your environment explicitly:
python3.11 -m venv venv
Then install with:
pip install -r requirements.txt
✅ MCP Server Ready
After boot:
- Proper loading summaries
- Safe confirmation before expensive actions
- Auto file watching and reindexing
- Windsurf plug-in ready
- Full tool coverage
You're good to cascade it! 🏄♂️
📈 Flow Diagram
+------------------+
| 🖥️ MCP Server |
+------------------+
|
+---------------------------------------------------+
| |
+-------------+ +-----------------+
| 📁 Corpora | | 🔎 FastMCP Tools |
| Folder | | (search, list, |
| (markdown, | | get_chunk, etc.) |
| code, etc) | +-----------------+
+-------------+ |
| |
+-----------------+ +----------------+
| 📚 Loaders | | 🧠 Ensemble |
| (Python, MD, TXT)| | Retriever (BM25|
| Split into Chunks| | + Chroma) |
+-----------------+ +----------------+
| |
+-----------------+ +----------------+
| ✂️ Text Splitter | | 🧩 Similarity |
| (RecursiveCharacter) | | Search (chunks) |
+-----------------+ +----------------+
| |
+-----------------+ +----------------+
| 💾 Embed chunks | —OpenAI Embedding (small)—> | 🛢️ Chroma Vector |
| via OpenAI API | | DB (Local Store) |
+-----------------+ +----------------+
| |
+-----------------+ +----------------+
| 📡 Reindex Watcher| | 👀 File Watchdog |
| (Auto detect | | (Auto reindex |
| new/modified files| | on file events) |
+-----------------+ +----------------+
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.