pickanagency

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

The description is consistent with annotations (readOnlyHint, openWorldHint). It adds behavioral detail by listing the returned fields (description, location, rating, services, website, recent reviews), which goes beyond the 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 a single sentence that succinctly conveys the purpose and key details without unnecessary words. It is well-structured 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 has 1 parameter, no output schema, and annotations providing readOnly/openWorld hints, the description adequately lists what the response contains. It could mention error cases (e.g., if slug not found), but overall it's sufficient.

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 one parameter 'slug'. The description adds an example ('clients-now-seo-agency-chennai') and explains the slug is the last path segment, adding meaning beyond the schema's minLength constraint.

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 'Fetch a single agency's full profile', which is a specific verb+resource. It distinguishes from sibling tools (match_agencies, search_agencies) by focusing on fetching a single agency by slug, while siblings are likely for finding or matching agencies.

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 the tool: when you have a specific slug (the last path segment of the profile URL). It does not explicitly mention when not to use or alternative tools, but the context is clear enough.

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 openWorldHint=true. The description adds context: the tool implements the same logic as the 'Get Matched' tool and returns a ranked shortlist, which is not stated in 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?

Two sentences, no wasted words. First sentence states purpose and resource, second gives usage context. Front-loaded with key information.

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 no output schema, the description adequately explains the output as a 'short ranked shortlist'. The tool has 7 parameters but only 1 required; description covers the essential inputs. Lacks details on ranking criteria but sufficient for tool selection.

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?

Input schema has 100% description coverage for all 7 parameters. The description mentions key parameters (services, location, budget, industry) but does not add new meaning beyond the schema descriptions. Baseline 3 is appropriate since schema already documents each parameter.

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 uses a matching engine to return a ranked shortlist based on buyer brief, distinguishing it from a raw search. Sibling tools include 'search_agencies', and the description explicitly contrasts with 'not just a raw search', 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?

Explicitly states when to use: 'Use this when someone wants a recommendation, not just a raw search.' This provides clear guidance on when to invoke this tool versus siblings like 'search_agencies'.

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 openWorldHint=true, so safety and result variability are covered. Description adds specific return fields (location, rating, reviews, profile link) and dataset size (47,000+). 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?

Two efficient sentences: first states purpose and scale, second lists filters and output. No superfluous text, well 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?

Covers dataset size, filter options, and key return fields. Does not explain result ordering, pagination (beyond limit param), or open-world implications (though annotation covers that). Adequate given 7 params and no output schema.

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%; parameters are fully documented. Description lists filter types with examples (e.g., SEO) but adds little new meaning beyond schema. Baseline 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?

Clearly states it searches a directory of 47,000+ marketing agencies with filtering capabilities. The verb 'Search' and resource 'directory' are specific. Distinguishes from siblings like get_agency (single agency retrieval) and match_agencies (likely alternative matching).

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?

Explains when to use: to find agencies by filtering on query, service, location, etc. Implies use case but does not explicitly mention when not to use or provide direct comparison to siblings. Clear context for usage.

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