ballast MCP server

ballast MCP server

Grounds local models with a quantized Wikidata corpus via MCP tools for entity resolution and fact lookup, reducing hallucinations.

Category
Visit Server

README

ballast

Pull a quantized knowledge corpus and ground any local model. Works with Ollama and every OpenAI-compatible or MCP-capable client.

uvx --from git+https://github.com/OpenBallast/ballast-cli openballast pull --level 3
uvx --from git+https://github.com/OpenBallast/ballast-cli openballast serve

(PyPI package coming — it becomes just uvx openballast.)

  • Ollama users: point your client's base URL at http://localhost:11435/v1 instead of http://localhost:11434/v1 — done. Every chat request is grounded with corpus facts before your model sees it. No tool calling needed, works with any model size.

  • MCP users (Claude Desktop, LM Studio, Cline, Goose): add to your MCP config:

    { "ballast": { "command": "uvx",
                   "args": ["--from", "git+https://github.com/OpenBallast/ballast-cli",
                            "openballast", "mcp"] } }
    
  • Smoke test:

    uvx --from git+https://github.com/OpenBallast/ballast-cli openballast lookup "Where was Douglas Adams born?"
    

What you're downloading

Ballast T0: 25.4M entities and 197M facts from Wikidata (CC0), quantized into nested levels — pick your knowledge size like you pick a GGUF quant:

level download on disk contains
L0 52 MB 0.2 GB top 0.5% most notable entities
L1 92 MB 0.35 GB top 1%
L2 159 MB 0.6 GB top 2%
L3 265 MB 1.0 GB top 4%
L4 427 MB 1.6 GB top 8%
L5 691 MB 2.6 GB top 16%
L6 1.1 GB 4.2 GB top 32%
L7 2.2 GB 9.2 GB everything

Levels are nested: pull --level 5 after pull --level 3 downloads only the new buckets. Everything runs offline after the pull — no network at answer time.

Measured effect (details: thesis): a 2B model + ~180 MB of ballast exceeds a 12B model's factual accuracy; hallucination on factual probes drops ~3×.

Commands

ballast pull  --level 3      # download / upgrade the corpus
ballast serve                # OpenAI grounding proxy :11435 + MCP http :11436
ballast mcp                  # MCP on stdio (for client configs)
ballast lookup "question"    # print the evidence blocks for a question
ballast status               # installed levels and sizes

BALLAST_HOME overrides the storage location (default ~/.ballast).

How it works

serve intercepts POST /v1/chat/completions, mines entity mentions from your last message, resolves them against the local corpus (normalized label/alias match), and prepends the matching facts as a system message. Everything else — including streaming — passes through untouched. The MCP server exposes the same three tools (resolve, evidence, lookup) as the hosted demo endpoint (mcp.openballast.org).

Apache-2.0. Corpus data: CC0 (Wikidata contributors).

Recommended Servers

playwright-mcp

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.

Official
Featured
TypeScript
Magic Component Platform (MCP)

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.

Official
Featured
Local
TypeScript
Audiense Insights MCP Server

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.

Official
Featured
Local
TypeScript
VeyraX MCP

VeyraX MCP

Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.

Official
Featured
Local
graphlit-mcp-server

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.

Official
Featured
TypeScript
Kagi MCP Server

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.

Official
Featured
Python
E2B

E2B

Using MCP to run code via e2b.

Official
Featured
Neon Database

Neon Database

MCP server for interacting with Neon Management API and databases

Official
Featured
Exa Search

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.

Official
Featured
Qdrant Server

Qdrant Server

This repository is an example of how to create a MCP server for Qdrant, a vector search engine.

Official
Featured