ColabMCP has 11 tools with complete input schemas and descriptions, but quality is uneven. Naming follows verb_noun convention well (add_code_cell, delete_cell, run_code_cell). Descriptions are present but often terse (avg ~60 chars, below the 194-char baseline). Parameters are typed with enums where appropriate (language: [python, r, julia]). However, output schemas are not documented, LLMs cannot predict what fields to expect from responses. Error handling is absent from descriptions. No tool annotations (readOnlyHint, destructiveHint, idempotentHint) despite clear risk levels assigned. Missing guidance on when to use similar tools (e.g., add_code_cell vs add_text_cell distinction is clear, but no recovery hints for failures).

• Add output schema documentation to every tool. Document response fields, types, and structure. E.g., run_code_cell should document: {output: string, execution_count: int, error?: {name: string, message: string}}.
• Expand descriptions to 100 - 200 chars. Include WHAT the tool does, WHEN to use it, and WHAT it returns. Example: 'Executes a code cell and returns its output. Use this to run analysis or setup code. Returns execution output, error details if failed, and execution count.'
• Add tool annotations: mark delete_cell and shutdown_runtime with destructiveHint=true; mark get_cells with readOnlyHint=true; mark set_env_vars with idempotentHint=true (if safe to retry).
• Add error recovery hints to descriptions. E.g., delete_cell: 'Deletes a cell. Cannot be undone, consider calling get_cells first to verify the cell ID. Returns error if cell not found.'
• Document open_colab_browser_connection input schema and purpose. Clarify whether it takes parameters (e.g., timeout, headless mode) and what it returns (connection handle, status).

Inferred effective spec: 2025-06-18+.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry