CommunityMentions

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

Annotations already declare readOnly/idempotent/non-destructive, so the description is free to add real behavioral context: result count cap (up to 10), ordering (closest match first), the fields returned (citation frequency, members, data-page link), and, crucially, the inclusion threshold (50+ citations or 50+ verified comments) and exclusions (health, legal-help, adult communities). That is information an agent cannot get from the schema or 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?

One dense paragraph that front-loads the trigger, then moves to inputs, outputs and filtering rules. Every sentence carries information, though the output-detail clause is a long run-on that could be split for easier scanning.

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 one-parameter read tool with a full schema and an output schema, the description still supplies the non-obvious pieces: what gets returned, how it is ranked, and which communities are excluded. Nothing needed to call or interpret the tool is missing.

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 coverage is 100% and there is a single parameter, so the baseline is 3, but the description goes further by showing the expected input shape with a concrete example ('AI writing tool for marketers') that clarifies how briefly and specifically to phrase the query.

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?

States a specific verb and resource ('Find subreddits for a product') and scopes it to a product, brand or niche. It is readily distinguishable from the singular sibling get_subreddit, which fetches one existing community.

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?

Gives an explicit trigger condition ('Use this when someone asks which subreddits ... fit a product') plus a second use case (finding where AI answers look for recommendations). It does not name the sibling get_subreddit or state when-not to use this, so the alternative routing is left implicit.

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

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

Annotations already declare readOnlyHint, idempotentHint, non-destructive and closed-world, so the safety profile is fully covered structurally. The description's additional content is a list of return fields, which is duplicated by the existing output schema rather than adding behavioral context (no error handling for unknown subreddits, no rate-limit or auth notes). Adequate but adds little beyond structured data.

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?

Two sentences, front-loaded with the usage trigger before the return summary. It is efficient, though the trailing enumeration of return fields partially duplicates the output schema and could be trimmed.

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?

With annotations covering the safety profile, a single fully-documented parameter, and an output schema handling return values, the description only needs to establish purpose and usage — which it does. Slight gap in not distinguishing when to fall back to find_subreddits.

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?

There is one parameter with 100% schema description coverage ('A subreddit name, with or without r/'). The description's examples ('SaaS' or 'r/marketing') restate exactly that accepted format, adding no new semantics, so the baseline 3 applies.

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 states a specific verb and resource (fetch AI-citation data for a subreddit) and enumerates the returned dimensions: citations by engine, question types, most-cited threads, member count. It is clearly distinct from the sibling find_subreddits, which is a discovery tool rather than a data-fetch tool.

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?

It gives concrete trigger conditions ('when someone asks how often AI answers cite a specific subreddit, which threads get cited, whether it matters for ChatGPT/Perplexity/Gemini'), which is strong when-to-use guidance. It never names find_subreddits as the alternative for lookup/discovery, so the routing is implied rather than explicit.

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