mcp-server-peecai

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

The description adds behavioral context beyond the annotations: it explains that suggestions can be 'accepted to create prompts or rejected to dismiss them,' indicating a workflow for managing suggestions. The annotations already cover safety (readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=false), so the description doesn't need to repeat those. It provides useful operational insight without contradicting the annotations, earning a good score for adding value.

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 a single, well-structured sentence that efficiently conveys the tool's purpose and key behavioral aspect. It is front-loaded with the main action and avoids unnecessary details, making it easy to parse. Every part of the sentence serves a clear purpose, earning a high score for conciseness.

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 (4 parameters, all optional), rich annotations (covering safety and idempotency), and the presence of an output schema, the description is reasonably complete. It explains the tool's role in managing prompt suggestions, which complements the structured data. However, it could be more explicit about how it differs from sibling tools, slightly reducing completeness.

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?

The description does not mention any parameters, leaving all semantic details to the input schema. Since the schema description coverage is 100%, the schema fully documents the parameters (project_id, topic_id, limit, offset) with descriptions and constraints. This meets the baseline of 3, as the description doesn't add extra meaning but the schema compensates adequately.

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 action ('List suggested prompts') and resource ('for a Peec AI project'), making the purpose understandable. However, it doesn't explicitly differentiate this tool from sibling tools like 'list_prompts' or 'list_topic_suggestions', which appear to be related listing operations. The description mentions that suggestions can be 'accepted to create prompts or rejected to dismiss them,' which adds context but doesn't fully distinguish the tool's unique role.

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 implies usage by mentioning that suggestions can be accepted or rejected, which suggests this tool is for reviewing and managing prompt suggestions. However, it doesn't provide explicit guidance on when to use this tool versus alternatives like 'list_prompts' or 'list_topic_suggestions' from the sibling list, nor does it specify prerequisites or exclusions. The usage context is somewhat implied but lacks clear differentiation.

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