mcp-server-peecai

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

The description adds valuable behavioral context beyond what annotations provide, including: the default behavior when date filters are omitted, explanation of empty results, and the fact that it returns actual search queries used by models. While annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, the description enhances understanding with practical usage details without contradicting annotations.

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 structured and concise with three focused sentences: the first states the core purpose, the second explains utility, and the third provides important behavioral guidance about defaults and empty results. Every sentence earns its place with no wasted words, and key information is front-loaded.

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?

Given the tool's moderate complexity (6 parameters, no output schema), the description provides good contextual completeness by explaining what the tool returns, its utility, default behaviors, and interpretation of empty results. The annotations cover safety and idempotency aspects well. The main gap is the lack of output format details, but this is somewhat mitigated by the clear purpose statement about returning 'actual search queries.'

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?

With 100% schema description coverage, the input schema already thoroughly documents all 6 parameters. The description doesn't add any parameter-specific information beyond what's in the schema descriptions, such as explaining the 'filters' array structure or 'limit' constraints. However, it does provide context about date filter behavior that complements the parameter documentation.

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 tool's purpose with specific verb ('Get search queries') and resource ('that AI models generated when answering prompts'), distinguishing it from siblings like 'shopping_queries' which likely handles different query types. It explicitly explains what the tool returns ('actual search queries models used to find information') and its utility ('for understanding how AI models research topics').

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 provides clear context about when to use this tool ('useful for understanding how AI models research topics') and includes important behavioral guidance ('Without date filters, returns data across all available dates'). However, it doesn't explicitly mention when not to use it or name specific alternatives among the sibling tools, which prevents a perfect score.

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