ims-mcp 1.0.5

Model Context Protocol server for IMS (Instruction Management Systems)

ims-mcp

Model Context Protocol (MCP) server for Rosetta (Enterprise Engineering Governance and Instructions Management System)

Powered by R2R technology for advanced RAG capabilities

This package provides a FastMCP server that connects to IMS servers for advanced retrieval-augmented generation (RAG) capabilities. It enables AI assistants like Claude Desktop, Cursor, and other MCP clients to search, retrieve, and manage documents in Rosetta knowledge bases.

Features

• 🔍 Semantic Search - Vector-based and full-text search across documents
• 🤖 RAG Queries - Retrieval-augmented generation with configurable LLM settings
• 📝 Document Management - Upload, update, list, and delete documents with upsert semantics
• 🏷️ Metadata Filtering - Advanced filtering by tags, domain, and custom metadata
• 🌐 Environment-Based Config - Zero configuration, reads from environment variables

Installation

Using uvx (recommended)

The easiest way to use ims-mcp is with uvx, which automatically handles installation:

uvx ims-mcp

Using pip

Install globally or in a virtual environment:

pip install ims-mcp

Then run:

ims-mcp

As a Python Module

You can also run it as a module:

python -m ims_mcp

Configuration

The server automatically reads configuration from environment variables:

Variable	Description	Default
R2R_API_BASE or R2R_BASE_URL	IMS server URL	http://localhost:7272
R2R_COLLECTION	Collection name for queries	Server default
R2R_API_KEY	API key for authentication	None
R2R_EMAIL	Email for authentication (requires R2R_PASSWORD)	None
R2R_PASSWORD	Password for authentication (requires R2R_EMAIL)	None

Authentication Priority:

1. If R2R_API_KEY is set, it will be used
2. If R2R_EMAIL and R2R_PASSWORD are set, they will be used to login and obtain an access token
3. If neither is set, the client will attempt unauthenticated access (works for local servers)

Note: Environment variables use R2R_ prefix for compatibility with the underlying R2R SDK.

Usage with MCP Clients

Cursor IDE

Local server (no authentication):

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "KnowledgeBase": {
      "command": "uvx",
      "args": ["ims-mcp"],
      "env": {
        "R2R_API_BASE": "http://localhost:7272",
        "R2R_COLLECTION": "aia-r1"
      }
    }
  }
}

Remote server (with email/password authentication):

{
  "mcpServers": {
    "KnowledgeBase": {
      "command": "uvx",
      "args": ["ims-mcp"],
      "env": {
        "R2R_API_BASE": "https://your-server.example.com/",
        "R2R_COLLECTION": "your-collection",
        "R2R_EMAIL": "[e-mail]",
        "R2R_PASSWORD": "your-password"
      }
    }
  }
}

Claude Desktop

Add to Claude Desktop configuration (~/Library/Application Support/Claude/claude_desktop_config.json on macOS):

{
  "mcpServers": {
    "ims": {
      "command": "uvx",
      "args": ["ims-mcp"],
      "env": {
        "R2R_API_BASE": "http://localhost:7272",
        "R2R_COLLECTION": "my-collection"
      }
    }
  }
}

Other MCP Clients

Any MCP client can use ims-mcp by specifying the command and environment variables:

{
  "command": "uvx",
  "args": ["ims-mcp"],
  "env": {
    "R2R_API_BASE": "http://localhost:7272"
  }
}

Available MCP Tools

1. search

Perform semantic and full-text search across documents.

Parameters:

• query (str): Search query
• filters (dict, optional): Metadata filters (e.g., {"tags": {"$in": ["agents"]}})
• limit (int, optional): Maximum results
• use_semantic_search (bool, optional): Enable vector search
• use_fulltext_search (bool, optional): Enable full-text search

Example:

search("machine learning", filters={"tags": {"$in": ["research"]}}, limit=5)

2. rag

Retrieval-augmented generation with LLM.

Parameters:

• query (str): Question to answer
• filters (dict, optional): Metadata filters
• limit (int, optional): Max search results to use
• model (str, optional): LLM model name
• temperature (float, optional): Response randomness (0-1)
• max_tokens (int, optional): Max response length

Example:

rag("What is machine learning?", model="gpt-4", temperature=0.7)

3. put_document

Upload or update a document with upsert semantics.

Parameters:

• content (str): Document text content
• title (str): Document title
• metadata (dict, optional): Custom metadata (e.g., {"tags": ["research"], "author": "John"})
• document_id (str, optional): Explicit document ID

Example:

put_document(
    content="Machine learning is...",
    title="ML Guide",
    metadata={"tags": ["research", "ml"]}
)

4. list_documents

List documents with pagination and optional tag filtering.

Parameters:

• offset (int, optional): Documents to skip (default: 0)
• limit (int, optional): Max documents (default: 100)
• document_ids (list[str], optional): Specific IDs to retrieve
• compact_view (bool, optional): Show only ID and title (default: True)
• tags (list[str], optional): Filter by tags (e.g., ["agents", "r1"])
• match_all_tags (bool, optional): If True, document must have ALL tags; if False (default), document must have ANY tag

Examples:

# List all documents (compact view - ID and title only)
list_documents(offset=0, limit=10)

# List with full details
list_documents(offset=0, limit=10, compact_view=False)

# Filter by tags (ANY mode - documents with "research" OR "ml")
list_documents(tags=["research", "ml"])

# Filter by tags (ALL mode - documents with both "research" AND "ml")
list_documents(tags=["research", "ml"], match_all_tags=True)

Note: Tag filtering is performed client-side after fetching results. For large collections with complex filtering needs, consider using the search() tool with metadata filters instead.

5. get_document

Retrieve a specific document by ID or title.

Parameters:

• document_id (str, optional): Document ID
• title (str, optional): Document title

Example:

get_document(title="ML Guide")

6. delete_document

Delete a document by ID.

Parameters:

• document_id (str, required): The unique identifier of the document to delete

Example:

delete_document(document_id="550e8400-e29b-41d4-a716-446655440000")

Returns:

• Success message with document ID on successful deletion
• Error message if document not found or permission denied

Metadata Filtering

All filter operators supported:

• $eq: Equal
• $neq: Not equal
• $gt, $gte: Greater than (or equal)
• $lt, $lte: Less than (or equal)
• $in: In array
• $nin: Not in array
• $like, $ilike: Pattern matching (case-sensitive/insensitive)

Examples:

# Filter by tags
filters={"tags": {"$in": ["research", "ml"]}}

# Filter by domain
filters={"domain": {"$eq": "instructions"}}

# Combined filters
filters={"tags": {"$in": ["research"]}, "created_at": {"$gte": "2024-01-01"}}

Development

Local Installation

Install directly from PyPI:

pip install ims-mcp

Or for the latest development version, install from source if you have the code locally:

pip install -e .

Running Tests

pip install -e ".[dev]"
pytest

Building for Distribution

python -m build

Requirements

• Python >= 3.10
• IMS server running and accessible (powered by R2R Light)
• r2r Python SDK >= 3.6.0
• mcp >= 1.0.0

License

MIT License - see LICENSE file for details

This package is built on R2R (RAG to Riches) technology by SciPhi AI, which is licensed under the MIT License. We gratefully acknowledge the R2R project and its contributors.

Links

• R2R Technology: https://github.com/SciPhi-AI/R2R
• Model Context Protocol: https://modelcontextprotocol.io/
• FastMCP: https://github.com/jlowin/fastmcp

Support

For issues and questions, visit the package page: https://pypi.org/project/ims-mcp/