ten-mcp-server

ten-mcp-server

MCP server exposing the Ten formal algebra for machine intelligence communication.

Ten treats messages as math, not natural language. This server gives LLMs structured access to Ten's algebraic operations via the Model Context Protocol.

Tools

Tool	Description
ten_encode	Build a Ten expression from a structured dict and return its binary wire format
ten_decode	Deserialize wire bytes back into a structured expression
ten_compose	Algebraically combine two expressions (sequence, product, nest, union, intersect)
ten_project	Extract a subset of facet dimensions from an expression
ten_filter	Evaluate expressions against facet-based filter criteria
ten_describe	Return a human-readable structural description of an expression
ten_verify	Validate expression tree integrity and check assertion metadata

All operations are pure code — no AI inference, no LLM calls. Encoding is building a data structure. Decoding is deserializing bytes. Filtering is numeric comparison.

Installation

One-click (MCP clients)

mcp install ten-mcp-server

From PyPI

pip install ten-mcp-server

From source

git clone https://github.com/johnbeans/Ten.git
cd Ten
pip install ./tenlang           # install Python bindings first
pip install ./ten_mcp_server    # install MCP server

Usage

As an MCP server (stdio transport)

ten-mcp-server
# or
python -m ten_mcp_server

Claude Desktop configuration

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "ten": {
      "command": "ten-mcp-server"
    }
  }
}

Expression Format

Expressions are JSON-friendly dicts with a type field:

# Scalar: a numeric value with dimension and precision
{"type": "scalar", "dimension": 0, "value": 42.0, "precision": "64bit"}

# Operation: a verb with optional arguments
{"type": "operation", "verb": "query", "args": [
    {"type": "scalar", "dimension": 1, "value": 100.0}
]}

# Assertion: a claim with confidence and identity
{"type": "assertion", "claim": {"type": "scalar", "dimension": 0, "value": 1.0},
 "who": {"type": "identity", "pubkey": "AAAA..."},
 "confidence": 0.95}

# Compositions: combine expressions algebraically
{"type": "sequence", "left": {...}, "right": {...}}
{"type": "nesting", "envelope": {...}, "payload": {...}}

Facets

Expressions can carry facet vectors — fixed-position sortable metadata:

urgency, cost, privilege, confidence, ttl, effort, reputation, value

Use ten_filter to evaluate expressions against facet criteria (e.g., "urgency >= 0.8 AND cost <= 50.0").

Dependencies

• tenlang — Python bindings for the Ten algebra (wraps libten C core)
• mcp — Model Context Protocol SDK

License

Apache-2.0 — Jolly Logic, LLC