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 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.
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.
Every tool call now creates a Runtime Session and Transaction, producing an immutable Version and collecting Diagnostics. This guarantees full auditability and reproducibility.
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.
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. |
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\": [], ...}"
}
]
}
}
Testing
python -m pytest -v
10 tests, all passing.
License
MIT
1 maintainer
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