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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It describes the two-step process (search then recommend) and location-based functionality, but it doesn't cover critical aspects like rate limits, authentication needs, error handling, or what the output looks like (since no output schema exists). For a tool with 11 parameters and no annotations, this is a significant gap in transparency.

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 appropriately sized with two sentences that are front-loaded with the core purpose. The first sentence states what the tool does, and the second explains the process and usage of parameters. There's minimal waste, though it could be slightly more structured for clarity.

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 complexity (11 parameters, no annotations, no output schema), the description is incomplete. It doesn't explain the return values or format (critical since no output schema exists), lacks details on behavioral traits like pagination or errors, and doesn't fully guide usage compared to siblings. For a recommendation tool with many parameters, this leaves significant gaps for an AI agent.

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%, so the schema already documents all parameters thoroughly. The description adds marginal value by explaining the interaction between 'q' and location parameters, but it doesn't provide additional syntax, format details, or examples beyond what the schema provides. With high schema coverage, the baseline is 3.

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 tool's purpose: 'Get personalized event recommendations based on performers, events, or location.' It specifies the verb ('Get'), resource ('event recommendations'), and basis ('performers, events, or location'). However, it doesn't explicitly differentiate from sibling tools like 'find_performer_recommendations' or 'find_events,' which reduces it from a perfect score.

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 implied usage guidance by mentioning that it 'first searches for performers and/or events based on the query (q parameter), then uses the IDs to find similar events' and suggests using location parameters for nearby events. However, it lacks explicit when-to-use vs. alternatives (e.g., compared to 'find_events' or 'find_performer_recommendations'), and no exclusions or prerequisites are stated.

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?

With no annotations provided, the description carries the full burden. It discloses the search logic (performer-first approach) and return format (structured event data with venue info), which is valuable. However, it doesn't mention potential limitations like rate limits, authentication needs, error conditions, or pagination behavior beyond what's in the schema, leaving gaps for a tool with 9 parameters.

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 efficiently structured in three sentences: first states the search purpose and criteria, second explains the optimization logic, third specifies the return format. Every sentence adds essential information without redundancy, making it appropriately sized and front-loaded.

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 complexity (9 parameters, no annotations, no output schema), the description is adequate but incomplete. It covers the core purpose and search logic well, but lacks details on error handling, performance expectations, or example use cases that would help an agent fully understand when and how to invoke it correctly.

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%, so the schema already documents all 9 parameters thoroughly. The description adds minimal value beyond the schema by mentioning search criteria (performer, location, date, venue) but doesn't provide additional syntax, format details, or usage nuances for parameters. Baseline 3 is appropriate when the schema does heavy lifting.

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 tool searches for events using specific criteria (performer, location, date, venue) and returns structured event data with venue information. It distinguishes itself from siblings like find_event_recommendations (which suggests events) and retrieve_event_venue_information (which focuses on venue details), establishing a specific search function.

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 on when to use this tool: for searching events based on user queries with specific filters. It explains the optimization logic (performer-first search). However, it doesn't explicitly state when NOT to use it or name alternatives like the sibling tools, missing full differentiation.

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?

With no annotations provided, the description carries the full burden of behavioral disclosure. It explains the two-step process (search then find similar) and mentions location-based filtering, which adds some context. However, it doesn't cover important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or what the output looks like (especially since there's no output schema). For a tool with 10 parameters and no annotations, this is insufficient.

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 appropriately concise with two sentences that efficiently convey the core functionality and location usage. The first sentence states the purpose clearly, and the second adds important behavioral context about the two-step process and location parameters. There's no unnecessary repetition or fluff, though it could be slightly more structured for optimal front-loading.

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 complexity (10 parameters, no annotations, no output schema), the description is incomplete. While it explains the basic process, it doesn't address critical contextual elements: what the output contains (formats, data structure), how pagination works with the 'page' and 'per_page' parameters, error handling, or performance considerations. For a recommendation tool with significant parameter complexity, this leaves too many gaps for effective agent use.

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 schema description coverage is 100%, so the schema already documents all 10 parameters thoroughly. The description adds marginal value by mentioning that location parameters (geoip, lat/lon, postal_code) can be used for nearby performers, though these specific parameters aren't in the schema. It also implies the relationship between 'performer_q'/'event_q' and the recommendation process. This meets the baseline for high schema coverage but doesn't significantly enhance parameter 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 clearly states the tool's purpose: 'Get personalized performer recommendations based on performers, events, or location.' It specifies the verb ('Get') and resource ('performer recommendations'), and mentions the three input bases (performers, events, location). However, it doesn't explicitly differentiate from sibling tools like 'find_event_recommendations' or 'find_events', which prevents a perfect score.

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 some implied usage context by explaining the tool's two-step process (search then find similar) and mentioning location parameters for nearby performers. However, it lacks explicit guidance on when to use this tool versus alternatives like 'find_event_recommendations' or 'find_events', and doesn't specify prerequisites or exclusions. This leaves room for ambiguity in tool selection.

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 are provided, so the description carries the full burden of behavioral disclosure. It adds some context: the tool 'first searches for the event using the provided query, then retrieves detailed venue layout information,' which explains the two-step process. However, it lacks details on permissions, rate limits, error handling, or what the output looks like (e.g., pagination details beyond the schema). This is a moderate effort but leaves gaps for a tool with no 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 appropriately sized and front-loaded: the first sentence states the core purpose clearly. The second sentence adds procedural context, and the third provides usage guidance. Each sentence earns its place without redundancy, making it efficient and well-structured. A minor deduction for slightly verbose phrasing in the second 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 annotations and no output schema, the description provides basic purpose and usage but lacks completeness. It doesn't detail the output format (beyond the 'format' parameter in the schema), error cases, or integration with sibling tools. For a tool with 3 parameters and no structured behavioral hints, more context on what to expect from the response would be helpful, leaving it adequate but with clear gaps.

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%, meaning the schema already documents all parameters well. The description doesn't add any parameter-specific semantics beyond what's in the schema (e.g., it doesn't explain 'event_id' further or provide examples). With high schema coverage, the baseline is 3, as the description doesn't compensate but also doesn't detract from the schema's documentation.

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 tool's purpose: 'Get detailed seating information including sections and rows for a specific event.' It specifies the verb ('get'), resource ('seating information'), and scope ('for a specific event'), making it easy to understand. However, it doesn't explicitly differentiate from sibling tools like 'find_events' or 'find_event_recommendations', which likely serve different purposes but aren't contrasted here.

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 some implied usage context: 'Useful for understanding venue seating options and making ticket purchasing decisions.' This suggests when to use the tool, but it doesn't explicitly state when not to use it or mention alternatives like sibling tools. For example, it doesn't clarify if this should be used instead of 'find_events' for seating details, leaving some ambiguity.

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