cks-mcp
Enables LLMs to perform structured, verifiable knowledge operations using the Canonical Knowledge Structure (CKS) ecosystem, including validation, querying, comparison, evolution, and derivation of knowledge.
README
CKS MCP Server
Model Context Protocol server for Canonical Knowledge Structure.
cks-mcp is an MCP (Model Context Protocol) server that provides LLMs
with structured, verifiable knowledge operations through the CKS
ecosystem. It exposes six tools—validate, query, compare, evolve,
derive, and construct—each backed by the deterministic, immutable
semantics of cks-core.
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.
Installation
pip install cks-mcp
The server requires cks-core (installed automatically as a dependency).
Quick Start
Launch the server:
cks-mcp
An MCP client (Claude Desktop, any MCP-compatible LLM) can then connect and call tools.
Available Tools
| Tool | Description |
|---|---|
validate_knowledge |
Validate a Knowledge Structure and return diagnostics. |
query_relations |
Find all relations for a given entity. |
compare_structures |
Check semantic equivalence of two structures. |
evolve_knowledge |
Apply Genesis/Decay operators to evolve a structure. |
derive_knowledge |
Derive a new Knowledge Object from existing premises. |
construct_knowledge |
Parse and construct a Knowledge Structure (coming soon). |
Usage Example
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "validate_knowledge",
"arguments": {
"json_data": "{\"objects\":[{\"identity\":{\"id\":\"obj-1\",\"type\":\"Definition\",\"name\":\"Test\"},\"structure\":{}}]}"
}
}
}
Response:
{
"jsonrpc": "2.0",
"id": 1,
"result": "{\"valid\": true, \"error_count\": 0, \"warning_count\": 0, \"diagnostics\": []}"
}
Testing
python -m pytest -v
20 tests, all passing.
Ecosystem
- cks-core — the canonical knowledge engine (repo)
- CKS Specifications — formal theory behind the system (DOI)
License
MIT
Recommended Servers
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.
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.
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.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
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.
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.
Neon Database
MCP server for interacting with Neon Management API and databases
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.
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
E2B
Using MCP to run code via e2b.