ResearchSpace Code Documentation Helper

A Model Context Protocol (MCP) server that helps you understand and navigate the ResearchSpace codebase. Ask questions about the code and get accurate answers with direct file references.

Purpose

This MCP server is designed to help developers understand the ResearchSpace codebase by:
- Answering questions about code structure and implementation
- Finding relevant code sections based on natural language queries
- Providing direct file references and code snippets
- Helping locate where specific functionality is implemented

Example Questions

- "How can I modify the Field Definitions?"
- "How can I create a field?"
- "Where does the parsing of a query happen?"
- "How is authentication implemented?"
- "Where are the SPARQL queries defined?"

Features

This MCP server provides tools for code documentation and exploration:

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#### 1. rs_ping
Returns the server version and operational status.

Parameters: None

Returns:
``json
{
"version": "1.0.0",
"status": "operational"
}
`

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#### 2. rs_search
Searches the indexed repository using semantic search. Returns relevant code chunks with similarity scores and file references.

Parameters:
- query (string, required): Search query (natural language or keywords)
- k (number, optional): Number of results to return (default: 8)
- scope (string, optional): "repo", "docs", "apps", or "all" (default: "all")

Returns:
`json
{
"hits": [
{
"id": "repo:src/main/java/org/researchspace/api/rest/client/FieldDefinition.java:0",
"source": "repo",
"filepath": "src/main/java/org/researchspace/api/rest/client/FieldDefinition.java",
"chunkId": 0,
"snippet": "public class FieldDefinition { ... }",
"score": 0.85,
"metadata": { ... }
}
],
"totalHits": 8
}
`

#### 3. rs_open
Opens and returns the full content of a specific search result chunk by its ID. Use this to get complete context after finding relevant code with rs_search.

Parameters:
- hitId (string, required): The ID of the search hit to open (from rs_search results)

Returns:
`json
{
"success": true,
"content": "Full chunk content here...",
"metadata": {
"source": "repo",
"filepath": "src/main/java/org/researchspace/api/rest/client/FieldDefinition.java",
"chunkId": 0,
"totalChunks": 5
},
"message": "Chunk retrieved successfully"
}
`

Documentation Workflow

The recommended workflow for answering code questions is: search → open → answer

1. Search: Use rs_search with a natural language query to find relevant code
2. Open: Use rs_open to get full context of the most relevant results
3. Answer: Provide accurate answers with direct file references

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Question: "How can I modify the Field Definitions?"

`javascript
// 1. Search for field definition code
rs_search({
query: "field definition modification create update",
k: 5,
scope: "repo"
})

// 2. Open the most relevant result
rs_open({
hitId: "repo:src/main/java/org/researchspace/api/rest/client/FieldDefinition.java:0"
})

// 3. Provide answer with file reference:
// "Field definitions can be modified in the FieldDefinition.java class located at
// src/main/java/org/researchspace/api/rest/client/FieldDefinition.java..."
`

Installation

`bash
npm install
`

Setup

Before first use, run the setup script to prepare the system:

`bash
npm run setup
`

This will:
1. Clone the repository: Downloads https://github.com/researchspace/researchspace to ~/.cache/rs-mcp/researchspace/ (~500MB-1GB)
2. Build the vector index: Processes all files, creates chunks, and generates embeddings

Time estimate: 10-20 minutes total
- Repository clone: 2-5 minutes (depending on internet speed)
- Initial indexing: 5-15 minutes (depending on CPU)

Note: The embedding model (~90MB) will be downloaded automatically during indexing.

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To refresh the index with the latest ResearchSpace code changes, simply re-run the setup:

`bash
npm run setup
`

This will pull the latest changes from the repository and rebuild the index.

Building

`bash
npm run build
`

Running

`bash
npm start
`

Development

`bash
npm run dev
`

Project Structure

`
/src
server.ts # Main MCP server implementation
setup.ts # Setup script for repository and indexing
rag/ # RAG implementation (vector DB, indexing, search)
/tests # Test files
README.md
LICENSE
`

Usage with MCP Clients

Add this server to your MCP client configuration:

`json
{
"mcpServers": {
"rs-code-docs": {
"command": "node",
"args": ["/path/to/rs_app_builder_mcp/dist/server.js"]
}
}
}
`

Example Questions and Queries

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`javascript
rs_search({
query: "How can I modify the Field Definitions?",
k: 5,
scope: "repo"
})
`

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`javascript
rs_search({
query: "How can I create a field?",
k: 5,
scope: "repo"
})
`

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`javascript
rs_search({
query: "Where does the parsing of a query happen?",
k: 5,
scope: "repo"
})
``

Technologies

- MCP SDK: Model Context Protocol implementation
- Vectra: Local vector database for embeddings
- Xenova Transformers: all-MiniLM-L6-v2 model for text embeddings
- TypeScript: Type-safe implementation

License

MIT