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thein-art

mcp-server-peecai

by thein-art

List Topics

list_topics
Read-onlyIdempotent

Retrieve topic IDs and names for a Peec AI project to organize and analyze brand visibility in AI-generated content.

Instructions

List topic groupings for a Peec AI project. Returns topic IDs and names.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
project_idNoProject ID (uses PEECAI_PROJECT_ID env if omitted). Call list_projects to find IDs.
limitNoMax results (1-10000)
offsetNoResults to skip

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
_summaryYesHuman-readable summary of the result
topicsYes
Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false, covering safety and idempotency. The description adds that it 'Returns topic IDs and names', which provides output context, but doesn't disclose additional behavioral traits like pagination behavior (implied by limit/offset) or rate limits. 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.

Conciseness5/5

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

The description is two sentences, front-loaded with the core purpose and followed by return value information. Every word earns its place with zero waste, making it highly efficient and easy to parse.

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

Completeness4/5

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, 100% schema coverage, and presence of an output schema, the description is mostly complete. It covers purpose and return values, though it could benefit from more explicit usage guidelines relative to siblings. The output schema means return values don't need explanation in the description.

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

Parameters3/5

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

Schema description coverage is 100%, with clear documentation for project_id, limit, and offset. The description adds no parameter-specific semantics beyond what the schema provides, such as explaining the relationship between parameters or usage nuances. With high schema coverage, the baseline score of 3 is appropriate.

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

Purpose4/5

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

The description clearly states the verb 'List' and resource 'topic groupings for a Peec AI project', specifying what the tool does. It distinguishes from siblings by focusing on topics rather than brands, chats, models, etc., but doesn't explicitly differentiate from 'list_topic_suggestions' which is a closely related sibling.

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

Usage Guidelines3/5

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

The description implies usage for retrieving topic IDs and names, but provides no explicit guidance on when to use this vs. alternatives like 'list_topic_suggestions' or other list tools. The input schema mentions 'list_projects' for finding IDs, which offers some contextual guidance, but this isn't in the description itself.

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

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