catchaset

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

With no annotations, the description fully bears the burden of behavioral disclosure. It explicitly states that results are taste-neutral, capped per call, and uses the same data as signed-out visitors. No contradictions or missing critical behaviors.

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?

Four sentences covering purpose, supported locations, filtering, behavior, and pagination. No redundant or verbose phrasing; each sentence adds value.

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 7 optional parameters, no output schema, and no annotations, the description adequately covers filtering, pagination, taste neutrality, and data freshness. It lacks details on return format but compensates with behavioral clarity.

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%, baseline 3. The description adds value by explaining city fallback (Austin if unsupported), case-insensitive substring matching for query, ISO date formats, and opaque cursor usage.

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 uses a specific verb 'Find' and resource 'upcoming live-music events' with clear filtering options (city, query, date range, genre). It also distinguishes itself from siblings by emphasizing 'taste-neutral' records and public data alignment.

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 clearly states when to use this tool (searching for public live music events in supported cities) and mentions pagination limits. However, it does not explicitly contrast with sibling tools or provide exclusions.

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?

The description discloses key behaviors: no account needed, ranking method (most matches then soonest), included why_matched field, and city default behavior. No annotations are provided, but the description covers safety and behavior adequately.

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 concise but packs necessary details about ranking, authentication, and result content. It front-loads the main action and is well-structured, though slightly wordy in the first sentence.

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 no output schema, the description explains the why_matched field and pagination via cursor. It covers city constraints and ranking, making it sufficiently complete for effective use, though additional output fields could be mentioned.

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 adds meaning beyond the schema by explaining that artists are the 'taste to match' and that results include why_matched. It also clarifies the city parameter's default and unsupported_city note, enriching understanding.

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 explicitly states the function: given user-liked artists, find upcoming shows in specific cities with ranking. It distinguishes itself by clarifying that genre-based discovery is not part of this tool, which aids selection.

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 explains when to use (when you have artists to match) and hints at when not to use (for genre discovery, use the web app). However, it does not directly contrast with sibling tools like find_events or overview.

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?

No annotations provided, so description carries full burden. It states 'No input' and implies a safe read operation. However, it doesn't explicitly declare idempotency, read-only nature, or any side effects. Adequate but not comprehensive.

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 output details, no wasted words. Efficient and to the point.

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 no output schema, the description explains return values (per-city counts, sources, refresh, staleness, coverage). It also notes no input and contexts siblings. Complete for a health-check tool.

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?

No parameters exist, so schema coverage is 100%. Guideline: with 0 params, baseline is 4. Description adds no parameter info because none are needed.

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 specifies the tool provides 'operational state of the CatchaSet event feed' and lists specific data (per-city event counts, data sources, etc.). It distinguishes from siblings by indicating it's a health/coverage check before using find_events.

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?

Explicitly says 'Call this to check health/coverage before relying on find_events.' This tells when to use it and implicitly when not to (when actual events are needed). Could mention alternatives more broadly, but clear enough.

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