CKS MCP Server

Model Context Protocol server for Canonical Knowledge Structure.

Python License Tests

cks-mcp is an MCP (Model Context Protocol) server that provides LLMs with structured, verifiable knowledge operations through the CKS ecosystem. It exposes four tools—validate_knowledge, serialize_knowledge, explain_knowledge, and evolve_knowledge—each backed by the deterministic, immutable semantics of cks-core and the operational management of cks-runtime.

Every tool call now creates a Runtime Session and Transaction, producing an immutable Version and collecting Diagnostics. This guarantees full auditability and reproducibility.

Ecosystem

CKS Core is the semantic foundation of the CKS ecosystem. Other projects build upon it:

Project Description Repository
cks-core Canonical semantic engine Deus-corp/cks-core
cks-runtime Operational environment – sessions, transactions, persistence Deus-corp/cks-runtime
cks-mcp MCP server – exposes CKS to LLMs (this repository) Deus-corp/cks-mcp

Why cks-mcp?

LLMs generate plausible but unverified statements. cks-mcp gives them a canonical knowledge backbone: every piece of information must be explicitly structured, validated against formal constraints, and traceable to its origin. This minimises hallucinations and makes AI‑ generated knowledge auditable.

In addition to the built‑in validation rules, validate_knowledge supports opt‑in extensions — extra, non‑default constraints that can be activated per call without affecting global state. The first available extension, embedding_projection, mechanically detects citation hallucinations: it verifies that every EmbeddingProjection points to a real source object that actually exists in the structure. This turns the abstract goal of "reducing hallucinations" into a concrete, machine‑checkable property of the knowledge graph.

Installation

pip install cks-mcp

The server requires cks-runtime (which includes cks-core) as a dependency.

Quick Start

Launch the MCP server

cks-mcp

An MCP client (Claude Desktop, any MCP-compatible LLM) can then connect and call tools.

Connect to Claude Desktop

  1. Install all three packages into a single virtual environment:

    python3 -m venv cks-env
source cks-env/bin/activate
pip install cks-core cks-runtime cks-mcp

  2. Open Claude Desktop, go to Settings → Developer → Edit Config. The configuration file (claude_desktop_config.json) will open. Add the following block (adjust the path to your cks-mcp executable):

    {
  "mcpServers": {
    "cks-mcp": {
      "command": "/absolute/path/to/cks-env/bin/cks-mcp"
    }
  }
}

  3. Save the file and fully restart Claude Desktop (Cmd+Q, then reopen). After restart, a connector icon will appear – cks-mcp with four tools is ready to use.

Interactive LLM client (Groq / DeepSeek / local)

export GROQ_API_KEY=your_key_here
python llm_client/cks_llm_client.py --provider groq

You can then type natural language requests; the LLM will automatically call the appropriate CKS tool.

Available Tools

Tool Description
validate_knowledge Validate a Knowledge Structure and return diagnostics. Supports opt‑in extensions (e.g. embedding_projection).
serialize_knowledge Serialize a Knowledge Structure into canonical JSON.
explain_knowledge Produce a semantic explanation of a Knowledge Structure.
evolve_knowledge Apply Genesis/Decay operators to evolve a structure.

Usage Example

{
  "method": "tools/call",
  "params": {
    "name": "validate_knowledge",
    "arguments": {
      "json_data": "{\"objects\":[{\"identity\":{\"id\":\"obj-1\",\"type\":\"Definition\",\"name\":\"Test\"},\"structure\":{}}]}"
    }
  }
}

Response (with version and session information):

{
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"valid\": true, \"version_id\": \"...\", \"session_id\": \"...\", \"diagnostics\": [], ...}"
      }
    ]
  }
}

Catching citation hallucinations

Pass "extensions": ["embedding_projection"] to validate_knowledge. This activates an extra constraint that checks every EmbeddingProjection object for a valid represents relation to an existing source object. A projection that references a non‑existent source (a fabricated citation) is mechanically flagged, giving you a clear, machine‑readable diagnostic instead of an undetected hallucination.

Testing

python -m pytest -v

19+ tests, all passing.

License

MIT

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (1.77.0)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for Deus-corp from gravatar.com Deus-corp

Credits

Author: Vladyslav Hruznov

License

MIT

Requires

Python >=3.12

Provides Extra

dev

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