MCP server for automated code review using AI agents
Both tools have minimal descriptions (under 100 chars) and lack parameter descriptions entirely. Input schemas are present but sparse: 'diff' and 'path' are typed as strings with brief descriptions, but lack constraints (format, length, examples of valid input). No output schema is documented. Tools return JSON strings rather than structured objects. Error handling is present (billing checks) but responses are JSON-encoded strings, not proper error objects. No parameter validation guidance. Naming is reasonable (review_code, check_quality) but descriptions don't explain WHEN to use each tool or what distinguishes them.
Run static analysis checks on a file path
Analyze a code diff for bugs, security issues, and style violations
No output schema documented. Tools return JSON-encoded strings; LLMs cannot infer response structure for downstream chaining.
Parameter descriptions are minimal (under 20 chars). 'diff' and 'path' lack format guidance, length constraints, or examples of valid input.
Tool descriptions (under 100 chars) lack context for LLM selection. No explanation of WHEN to use review_code vs check_quality, or what each returns.
Error responses are JSON-encoded strings, not structured error objects. LLMs cannot reliably parse or act on billing errors.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 46 | 2026-07-28+ | v2 |
No input validation or constraint documentation. 'diff' could be empty or gigabytes; 'path' could be absolute, relative, or invalid. No guidance on limits.