CKS MCP Server

Model Context Protocol server for Canonical Knowledge Structure.

Python License Tests PyPI

cks-mcp is an MCP (Model Context Protocol) server that gives LLMs a canonical knowledge backbone. It exposes the tools listed under Available Tools below, backed by the deterministic, immutable semantics of cks-core and the operational management of cks-runtime.

Every tool call 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

Quick Start

  1. Install and connect to Claude Desktop (see Installation).
  2. In the chat, start your message with "Use cks-mcp to…".
  3. Claude automatically picks the right tool from the 13 available — validation, evolution, branching, merging, source verification, subgraph queries, and more.
  4. Every operation is logged, versioned, and stored in a persistent SQLite database.

Just type "Use cks-mcp to..." and Claude does the rest. That's it. No programming, no command line — just a conversation!

CKS Demo

In the video above, Claude creates a validated knowledge graph about the water cycle from a single sentence, using validate_knowledge and explain_knowledge. Fifteen tools are ready for you: branching, merging, versioning, source verification, subgraph queries, and more — all triggered by plain English.

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.

  • Eliminate citation hallucinations — optional extensions like embedding_projection mechanically detect references to non-existent sources.
  • Ensure verification integrity — the verify_source tool performs a real HTTP check and cryptographically signs the result. Any VerificationRecord without a valid signature is automatically rejected, even if the model fails to request the check.
  • Full audit trail — every operation is captured in an immutable version history, providing complete accountability for AI-generated knowledge.
  • Time-travel debugging — list_versions, revert_version, and compare_versions give LLMs a full version-control system for knowledge, enabling safe rollbacks and change inspection.

Installation

pip install cks-mcp

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

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 fifteen 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 (embedding_projection, verification_record). Provenance of VerificationRecord objects is checked automatically.
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.
verify_source Perform a real HTTP request to check a URL's availability and create a cryptographically signed VerificationRecord.
list_versions List all available versions of a session's history.
compare_versions Compute the structural difference between the current state of a session and a target version.
revert_version Revert a session's Knowledge Structure to a specific previous version.
merge_knowledge Three-way merge of knowledge structures with conflict detection.
create_branch Fork a new session from an existing one, optionally from a specific historical version.
merge_branch Session-aware three-way merge: merge a branch session into a target session, resolving the merge base automatically from the branch's recorded fork point.
close_session Close a session, releasing it from the runtime (e.g. a branch already merged in).
query_subgraph Extract a local k‑hop neighbourhood from a session's Knowledge Structure, with filters and optional budget.
search_semantic Semantically search a session's Knowledge Structure using natural language and seed IDs, expanding the neighbourhood with query_subgraph.
get_metrics Return runtime metrics: invocation counts and average execution times per operation type.

Usage Examples

Validate a structure with citation-hallucination detection

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.

Validate a structure with verification integrity

When you use verify_source to check a URL, the resulting VerificationRecord is cryptographically signed. Any VerificationRecord found in a structure without a valid signature is automatically rejected, even if the model does not explicitly request the verification extension. This prevents LLMs from bypassing the check by simply omitting a parameter.

Basic validation

{
  "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\": [], ...}"
      }
    ]
  }
}

Compare two versions

{
  "method": "tools/call",
  "params": {
    "name": "compare_versions",
    "arguments": {
      "session_id": "...",
      "target_version_id": "..."
    }
  }
}

Response:

{
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"session_id\": \"...\", \"target_version_id\": \"...\", \"changes\": [...]}"
      }
    ]
  }
}

Branch, evolve independently, and merge back

Fork a session, evolve the branch and its parent independently, then merge the branch back in:

{"method": "tools/call", "params": {"name": "create_branch",
  "arguments": {"session_id": "trunk-session-id"}}}

{"method": "tools/call", "params": {"name": "evolve_knowledge",
  "arguments": {"session_id": "branch-session-id", "operations": [...]}}}

{"method": "tools/call", "params": {"name": "merge_branch",
  "arguments": {"target_session_id": "trunk-session-id",
                "source_session_id": "branch-session-id"}}}

A successful merge commits a new version of the target session and returns its serialized structure and version_id. A conflicting merge instead returns "merged": false with a conflicts list (object_id, base_state, target_state, source_state) — resolve each one on the target session with evolve_knowledge, then close_session the branch once it's fully integrated.

Query a subgraph

{
  "method": "tools/call",
  "params": {
    "name": "query_subgraph",
    "arguments": {
      "session_id": "...",
      "seed_ids": ["obj-1"],
      "depth": 2,
      "max_objects": 10
    }
  }
}

Response (truncated example):

{
  "result": {
    "content": [
      {
        "type": "text",
        "text": "{\"subgraph\": \"...\", \"total_found_nodes\": 15, \"returned_nodes\": 10, \"is_truncated\": true, \"suggested_next_seed\": \"obj-7\"}"
      }
    ]
  }
}

Security and Provenance

verify_source includes built-in protections:

  • SSRF prevention: URLs are validated against a strict allowlist; private, loopback, and cloud metadata IPs are blocked. DNS rebinding attacks are neutralised by pinning the connection to the IP address resolved during the safety check.
  • Cryptographic signing: every verification record is signed with a process-local HMAC. validate_knowledge unconditionally verifies this signature, so a hand‑written VerificationRecord can never pass as genuine.

Testing

python -m pytest -v

50+ 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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