Tacitbase MCP Server

Tacitbase MCP Server

By sourabh-khot65 GitHub

MCP server for connecting to you typesense collections and retrieve data using your favorite MCP client (Claude/ Cursor)

Overview

what is Tacitbase MCP Server?

Tacitbase MCP Server is a Machine Comprehension Protocol (MCP) server designed to connect to Typesense collections, enabling advanced search capabilities through both Tacitbase's native search and Typesense.

how to use Tacitbase MCP Server?

To use the server, install the dependencies, build the server, and run it. You can then perform searches using various methods such as basic, vector, and semantic search.

key features of Tacitbase MCP Server?

  • Basic keyword search with filtering and sorting
  • Vector search for similarity-based matching
  • Semantic search with natural language understanding
  • Fallback to Tacitbase's native search
  • Tool registration for searching candidates

use cases of Tacitbase MCP Server?

  1. Finding candidates based on specific skills and experiences.
  2. Conducting similarity searches to identify candidates with similar profiles.
  3. Utilizing natural language queries to enhance search results.

FAQ from Tacitbase MCP Server?

  • What programming language is used for the server?

The server is built using Go.

  • How do I configure the server?

You can configure the server using environment variables for authentication and connection settings.

  • Can I extend the server functionality?

Yes! You can modify the main.go file to add new tools or modify existing ones.

Content

Tacitbase MCP Server

This is a Machine Comprehension Protocol (MCP) server for Tacitbase that provides advanced search capabilities using both Tacitbase's native search and Typesense for enhanced search features.

Project Structure

The project follows a clean, modular architecture:

.
├── models/      # Data structures and types
├── services/    # Business logic and external service interactions
├── handlers/    # Request handlers and routing logic
├── tools/       # Tool registration and configuration
├── main.go      # Application entry point
└── README.md    # Project documentation

Packages

  • models: Contains all data structures and types used across the application
  • services: Implements the business logic and external service interactions
  • handlers: Contains the request handlers that process incoming requests
  • tools: Registers the tools available for searching candidates

Features

  • Basic keyword search with filtering and sorting
  • Vector search for similarity-based matching
  • Semantic search with natural language understanding
  • Fallback to Tacitbase's native search when needed
  • Tool registration for searching candidates

Search Tools

1. Basic Search (search_candidates)

  • Keyword-based search with typo tolerance
  • Supports field-specific search
  • Filtering and sorting capabilities
  • Group by functionality
  • Exact matching option

2. Vector Search (vector_search_candidates)

  • Similarity search using vector embeddings
  • Ideal for finding candidates with similar profiles
  • Supports hybrid search combining vectors with filters

3. Semantic Search (semantic_search_candidates)

  • Natural language understanding
  • Automatic embedding generation
  • Support for multiple embedding models (OpenAI, SBERT, E5)
  • Hybrid search combining semantic understanding with filters

Configuration

Environment Variables

  • TACITBASE_AUTH_TOKEN: Authentication token for Tacitbase API
  • TYPESENSE_API_KEY: API key for Typesense
  • TYPESENSE_HOST: Typesense host (default: localhost)
  • TYPESENSE_PORT: Typesense port (default: 8108)
  • TYPESENSE_PROTOCOL: Protocol for Typesense (default: http)

Installation

  1. Install dependencies:
go mod download
  1. Build the server:
go build -o tb-mcp-server
  1. Run the server:
./tb-mcp-server

Example Usage

{
  "query": "golang developer",
  "search_fields": "skills,latest_experience",
  "filter_fields": "location:San Francisco",
  "sort_by": "latest_experience:desc",
  "page": 1,
  "per_page": 20
}

{
  "vector_query": "[0.1, 0.2, ..., 0.512]",
  "filter_fields": "years_of_experience:>5",
  "page": 1,
  "per_page": 20
}

{
  "query": "experienced team lead with cloud architecture background",
  "embedding_model": "openai",
  "filter_fields": "years_of_experience:>8",
  "page": 1,
  "per_page": 20
}

Development

To modify or extend the server:

  1. Edit main.go to add new tools or modify existing ones
  2. Run tests (if any)
  3. Build and test the server
No tools information available.
No content found.