Fast code intelligence engine for AI coding agents — single static binary MCP server
Single tool 'index_repository' has a basic description but critically lacks parameter descriptions and a documented output schema. The input schema defines two parameters (repo_path, mode) with types but no descriptions explaining what 'mode' values are valid, what 'full' means, or what the tool returns. No error handling guidance. Tool naming is acceptable (verb_noun form) but the implementation is far below production baseline. The server appears to be a code intelligence indexer, but the tool definition provides insufficient context for an LLM to use it effectively.
Index a codebase repository to build a code intelligence graph
Parameter 'mode' lacks description. LLM cannot infer valid values (e.g., 'full', 'incremental', 'fast'). Undocumented parameters force LLMs to guess.
Parameter 'repo_path' description is missing. Should specify: absolute vs relative paths, whether it must exist, symlink handling, and max path length.
No output schema documented. LLM cannot plan downstream operations or extract results. What does the tool return? Success/failure? Graph structure? File count?
No error handling guidance. If indexing fails (permission denied, invalid path, timeout), LLM has no recovery path. Error responses must guide next steps.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 48 | <=2025-11-25 | v2 |
Tool description (current: 'Index a codebase repository to build a code intelligence graph') lacks WHEN/WHY context. When should an LLM call this vs other tools? What prerequisites exist?