mcp-server-peecai

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

Annotations already provide readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds context about what's returned (model IDs and active status) but doesn't disclose additional behavioral traits like rate limits, authentication needs, or pagination details beyond what's in the schema. No contradiction with 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 front-loaded with the core purpose in the first sentence and adds return details in the second. It's appropriately sized with zero waste—every sentence provides essential information without redundancy or fluff.

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 low complexity (a simple list operation), rich annotations (covering safety and idempotency), high schema coverage (100%), and the presence of an output schema (which handles return value documentation), the description is complete enough. It effectively communicates what the tool does without needing to over-explain.

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%, with all parameters well-documented in the schema (project_id, limit, offset). The description doesn't add any parameter-specific information beyond what the schema provides, such as explaining default behaviors or usage nuances. Baseline score of 3 is appropriate given high schema coverage.

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 ('List AI models tracked by Peec AI') and identifies the resource ('AI models'). It distinguishes from siblings by specifying the exact type of models (ChatGPT, Perplexity, etc.) and what information is returned (model IDs and active status), which is different from other list_* tools like list_brands or list_projects.

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 what the tool does (lists AI models with specific attributes), but it doesn't explicitly state when to use this tool versus alternatives like list_model_channels or other sibling tools. It implies usage for retrieving model information but lacks explicit guidance on exclusions or comparisons.

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