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Official STRING Database MCP Server

Official STRING Database MCP Server

README.md
# Official STRING Database MCP Server

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Exposes [STRING database](https://string-db.org) functionality as a **Model Context Protocol (MCP)** server.  

This implementation allows AI agents and other MCP-compatible clients to access STRING data through a structured and self-describing interface.

It is built on top of the STRING API but adapted specifically for model-based use, with emphasis on conciseness and context efficiency. The server controls the amount and type of data so that responses stay within the reasoning limits of large language models. It also adapts the structure and adds metadata to support more consistent parsing and interpretation by agentic systems.

The server is also available as a hosted remote MCP endpoint at [https://mcp.string-db.org](https://mcp.string-db.org).

For a live demonstration of MCP capabilities, see [STRING Chat](https://string-db.org/cgi/chat).

For reproducible workflows and large-scale integration, use the [STRING API](https://string-db.org/cgi/help?subpage=api).

For agentic workflows, use the optional agent skill at [skills/string-database/SKILL.md](skills/string-database/SKILL.md).

## Features

- Resolve protein identifiers to STRING metadata
- Retrieve interaction networks
- Cluster and visualize interaction networks
- Perform homology lookups across species
- Access evidence links for protein–protein interactions
- Run functional enrichment analysis (including enrichment plots)
- Get curated functional annotations for proteins
- Query proteins by function or disease (GO, KEGG, Reactome, DISEASES etc.)

---

## Requirements

- **Python** ≥ 3.11 (with Python 3.10, optional parameter descriptions are misplaced in the tool schemas)  
- Dependencies (see `requirements.txt`):
  - `fastmcp==2.14.7`
  - `mcp==1.29.0`
  - `httpx>=0.28,<0.29`
  - `pydantic>=2.11,<3.0`

> **Note:** If the server crashes on startup, it is very likely due to an incompatible **FastMCP** version.

---

## Configuration

The server reads settings from `config/server.config` (JSON). Example:

```json
{
  "base_url": "https://string-db.org",
  "server_port": 57416,
  "verbosity": "low"
}
```

## Installation

```bash
git clone https://github.com/meringlab/string-mcp.git
cd string-mcp
pip install -r requirements.txt
```

## Running 

```
python server.py
```

To serve over standard input/output instead of HTTP, for example when a local MCP client starts the server itself:

```
python server.py --stdio
```

The stdio mode also reads `config/server.config` for the STRING API endpoint.

## Testing

   ```bash
   curl -v -H "Accept: text/event-stream" http://localhost:[port]/mcp
   ```

You should see a `200 OK` response with `content-type: text/event-stream`. 

**Note:** HTTP requests sent with default headers will trigger `anyio.ClosedResourceError` and `406 Not Acceptable`. This is expected behavior for the MCP streaming protocol and does not indicate a problem.

## Docker

1. Ensure you have a `config/server.config` file (copy from `config/server.config.example` if needed):
   ```bash
   cp config/server.config.example config/server.config
   ```

2. Build the image:
   ```bash
   docker build -t string-mcp .
   ```

3. Run the container:
   
   **Linux/macOS:**
   ```bash
   docker run -p 57416:57416 -v $(pwd)/config:/app/config:ro string-mcp
   ```
   
   **Windows PowerShell:**
   ```powershell
   docker run -p 57416:57416 -v ${PWD}/config:/app/config:ro string-mcp
   ```
   
   **Windows Command Prompt:**
   ```cmd
   docker run -p 57416:57416 -v %cd%/config:/app/config:ro string-mcp
   ```

   To run in detached mode:
   ```bash
   # Linux/macOS
   docker run -d -p 57416:57416 -v $(pwd)/config:/app/config:ro --name string-mcp-server string-mcp
   
   # Windows PowerShell
   docker run -d -p 57416:57416 -v ${PWD}/config:/app/config:ro --name string-mcp-server string-mcp
   ```

   The `-v` flag mounts your local config directory so you can customize settings without rebuilding the image.
   
   **Note:** If you don't need to modify the config, you can omit the volume mount and the container will use the default config from the image.

4. Stop the container (if running in detached mode):
   ```bash
   docker stop string-mcp-server
   docker rm string-mcp-server
   ```

**Note:** The Docker container will use the configuration from `config/server.config`. If this file doesn't exist, the container will copy from `server.config.example` on first run.


Frozen STRING releases are available under versioned paths: `/12.0/` (STRING v12.0) and `/12.5/` (STRING v12.5).


## License / Citation

The STRING MCP server source code is released under the [MIT License](https://opensource.org/licenses/MIT).  
Associated data and outputs are released under the [CC BY 4.0 License](https://creativecommons.org/licenses/by/4.0/).  
You are free to use, share, and modify the code with proper attribution.

If you use this code or data in your work, please also cite the latest [STRING manuscript](https://string-db.org/cgi/about?footer_active_subpage=references).

TDQS

A4.1/5.0

Scored across 17 tools

Disambiguation4/5

Most tools have clearly distinct purposes, but several adjacent pairs (visual_network vs network_link, interactions_query_set vs all_interaction_partners, enrichment vs ppi_enrichment) share similar inputs and could be confused when the user's wording is vague. The descriptions clarify boundaries well enough to avoid major misselection.

Naming Consistency4/5

All names use a consistent string_ snake_case prefix and are readable, but many are noun phrases (string_visual_network, string_enrichment) rather than following a uniform verb_noun pattern. The style is still predictable with only minor deviations.

Tool Count4/5

17 tools is slightly heavy but reasonable for STRING's broad functionality: identifier resolution, interactions, visualization, enrichment, homology, species lookup, export, and help. Each tool appears to cover a distinct capability rather than being redundant.

Completeness5/5

The surface covers STRING's main workflows, including species lookup, protein resolution, sequence search, interaction retrieval, network visualization/link/clustering, evidence lookup, functional annotation, functional and PPI enrichment, term lookup, file export, and help. No obvious domain gap is apparent, though some visualization and enrichment tools are closely related.

Maintenance

ActivityMaintained
ResponsivenessLimited data