pifini

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

Adds context beyond annotations: describes the payment flow (returns URL, tickets emailed after payment), which is consistent with the readOnlyHint=false and destructiveHint=false 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?

Three concise sentences with no waste, front-loaded with the core purpose.

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?

Covers all aspects: input, output (checkout URL), side effects (email after payment), and prerequisites (get_event), making the tool self-contained.

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?

With 100% schema coverage, the description adds value by clarifying the buyer_email is required and should be asked from the human, and that ticket_type_id comes from get_event.

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?

Clearly states the tool creates a Stripe-hosted payment link for event tickets, distinguishing it from sibling tools like get_event which are read-only.

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 advises confirming ticket details with the human before calling and references get_event for ticket_type_id, but does not explicitly state when not to use.

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=true, destructiveHint=false, and idempotentHint=true. The description adds value by explicitly enumerating the returned fields (venue, date/time, ticket types with prices and remaining stock), giving the agent a preview of the response content beyond what annotations convey.

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 two concise sentences, front-loaded with the core purpose and then a practical usage hint. No redundant words or repetition of structured fields, making every sentence earn its place.

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 one simple parameter, high schema coverage, an output schema, and complete annotations, the description adequately covers the tool's behavior and usage. It mentions the key response elements, making it self-sufficient for a straightforward getter.

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% with a description 'Event slug from search_events'. The tool description repeats this with 'Use the slug from search_events', reinforcing but not adding new meaning beyond the schema. The baseline is 3 given high schema coverage.

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 it provides 'Full details for one event by slug' and lists specific response components (description, venue, date/time, ticket types with prices and stock). This distinguishes it from siblings like search_events, which find events, and get_place, which retrieves place details.

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 tells the user to 'Use the slug from search_events', providing a clear prerequisite and source for the input. It implies this tool is for fetching a specific event's full details, but does not explicitly state when not to use it or offer alternative tool comparisons beyond the slug hint.

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?

Beyond the readOnly and idempotent annotations, the description adds semantic context: recent_reviews being an empty array means no reviews yet (not low quality), and listing_type distinguishes curated vs directory entries. This helps interpret results.

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 compact and front-loaded, with the main purpose in the first sentence and useful interpretive context in the second. It is efficient, though the phrase 'Use the slug from search_places' somewhat duplicates schema info.

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 simple single-parameter, read-only tool, the description provides adequate context: what the tool does, where the slug comes from, and key behavioral traits of the response (reviews and listing_type). The presence of an output schema covers the need to enumerate all fields.

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 only parameter, slug, is fully described in the schema as 'Place slug from search_places'. The description just repeats this instruction without adding new format details, so it remains at the schema-coverage baseline.

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 it retrieves full details for a single place by slug, including reviews. This distinguishes it from siblings like search_places and get_event by focusing on slug-based individual lookup.

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 instructs to use the slug from search_places, establishing a workflow and referencing the alternative tool for context. However, it does not explicitly say when not to use this tool (e.g., for filtered or searched lists), leaving some room for inference.

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, and destructiveHint. The description adds context about the taxonomy (Overture Maps) and the need for exact values, which goes beyond the annotations. No contradictions.

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 (4 sentences) and front-loaded with purpose. Every sentence adds value: purpose, taxonomy explanation, and usage workflow. No wasted words.

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 simple nature of the tool, rich annotations, and presence of output schema, the description covers all necessary aspects: purpose, usage, parameter roles, and relation to sibling tools. It is fully complete for an agent to use 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 coverage is 100% with parameter descriptions. The description adds workflow context and examples (e.g., 'coffee', 'beach'), which enhances understanding beyond the schema alone.

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: 'Discover valid category values for search_places.' It uses a specific verb and resource, and distinguishes itself from sibling tools by explaining its role as a preliminary step for search_places.

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 explicit usage guidance: 'Call this first with a rough query... then pass it to search_places' category parameter.' It explains when to use the tool and why exact values are necessary, effectively directing the agent's workflow.

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=true and destructiveHint=false, so the safety profile is covered. The description adds value by noting coverage limitations ('Strongest coverage: Barbados, Trinidad...') and return field specifics including the 'price (when known)' caveat, which helps set expectations.

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 two sentences with no waste. It front-loads the action ('Search upcoming events...') and scope, then concisely lists return fields and coverage strengths.

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 rich schema (13 params, all documented) and the presence of an output schema, the high-level description is sufficient. It orients the agent to the tool's purpose and geographic focus without needing to restate parameter semantics. Slightly more context about filtering capabilities would push it to 5, but it is complete enough.

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?

All 13 parameters have detailed descriptions in the schema (100% schema description coverage), including nuanced explanations for starts_after and starts_before date handling. The tool description adds no parameter-level detail beyond what the schema already provides, so 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.

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

The description clearly states it searches upcoming events (festivals, carnivals, concerts, food + cultural events) across a specific geography. It distinguishes itself from sibling tools like search_places and get_event by focusing on multi-event search for the Caribbean/Latin America region.

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 provides clear context for when to use the tool (event discovery in specific regions) but does not explicitly mention when not to use it or name alternatives. The context is sufficient for an agent to infer it is the right tool for finding events as opposed to places or single-event details.

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 mark this as read-only and non-destructive, so the description adds valuable context about result types (curated vs directory), data provenance, and disclosure requirements. 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.

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

The description is composed of three well-organized sentences, each conveying essential information: scope, result interpretation, and category taxonomy. No filler or redundancy.

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?

The description covers critical operational nuances including regional scope, data quality tiers, category specificity, and sponsored content handling. With an output schema present, this is sufficiently complete for accurate 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 input schema already provides descriptions for all 10 parameters (100% coverage). The tool description adds minimal parameter-specific context beyond reiterating the category guidance already present in the schema, so it stays at the baseline 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?

Description clearly states the tool searches Pifini's places directory across a specific region, listing the types of places covered. It distinguishes from siblings like search_events and get_place through resource specificity, making the purpose unambiguous.

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?

Provides explicit guidance on when to use the tool and directs users to list_categories first for valid category values. It also gives behavioral instructions for interpreting curated vs directory results and disclosing sponsored content, going beyond basic usage.

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