LLM semantic memory with 29 tools for AI agents using Geometric Algebra and Bagua taxonomy for knowledge graphs
Critical gaps across all dimensions. Tool definitions are inferred from Python source (iching_mcp/server.py) rather than explicitly visible in Rust MCP registration. Descriptions are present but minimal (10-80 chars, well below 50-200 char baseline for LLM optimization). Input schemas visible in Python but lack type definitions and constraints, parameters are documented as arrays/strings with minimal guidance on structure, format, or valid values. No output schemas documented. No error handling guidance. No parameter validation rules or dependency documentation. The Rust HTTP server (ga-semantics-mcp) has no visible tool definitions in provided source, only Cargo.toml and Dockerfile. Python tools appear to be a separate implementation not integrated into the Rust MCP server.
Evaluate Clifford algebra Cl(3) operations on blades and rotors
Audit task coverage against plan using 8-role checklist from I-Ching
Compute interaction spectrum from points and values in Cl(3) blade algebra
Reframe a statement using 8 cube-move perspectives from I-Ching
Evaluate rotor algebra operations for geometric transformations
Summarize 3-aspect state difference between before and after states
Tool definitions inferred from Python source, not explicitly registered in visible Rust MCP code. No tool registration, schema binding, or capability declaration visible in ga-semantics-mcp Rust source.
Input schemas lack type definitions and constraints. Parameters documented as 'array' or 'string' with minimal guidance on structure, format, length, or valid values. No enums, patterns, or ranges specified.
Descriptions are minimal (10-80 chars) and lack LLM-optimization guidance. Missing WHAT the tool does, WHEN to use it, and what it returns. No dependency hints or prerequisite documentation.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 35 | 2026-07-28+ | v2 |
No output schemas documented. LLMs cannot plan downstream tool calls or extract required data without knowing response structure.
No error handling guidance. Error responses do not indicate whether failures are retryable, user-fixable, or fatal. No recovery suggestions provided.