TuringMind MCP Server

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states this is a feedback submission tool which implies a write/mutation operation, but doesn't specify authentication requirements, rate limits, or what happens after submission (e.g., confirmation, error handling). The description adds some context about the purpose but lacks operational details needed for full transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is perfectly concise with two sentences that each earn their place. The first sentence states the purpose and usage context, while the second provides specific guidance for false positives. No wasted words, and the information is front-loaded appropriately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a mutation tool with no annotations and no output schema, the description does well by clearly stating purpose, usage guidelines, and parameter relationships. However, it lacks details about authentication requirements, error handling, or confirmation of submission success, which would be helpful given this is a write operation with no output schema to document results.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds value by explaining the relationship between parameters: it clarifies that 'pattern' and 'reason' are especially important for 'false_positive' actions, providing semantic context beyond the schema's individual parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the specific action ('submit feedback') on a specific resource ('code review issue') and distinguishes it from siblings by focusing on feedback submission rather than context retrieval, authentication, or review upload. It provides concrete examples of when to use it ('issue was fixed, should be dismissed, or is a false positive').

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('when user indicates an issue was fixed, should be dismissed, or is a false positive') and provides specific guidance for false positives ('provide pattern and reason to improve future reviews'). It clearly differentiates this from sibling tools that handle authentication, context retrieval, or review upload.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.