gankdat

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

With no annotations provided, the description carries the full burden. It states it's free to call, implying no side effects. For a simple read operation with no parameters, this is sufficiently transparent, though it could mention authentication assumptions.

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 with zero waste. The key information (purpose, what it returns, cost hint) is front-loaded and efficient.

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 simplicity (no parameters, no output schema), the description covers all necessary context: it states what data is returned (plan, credits used/granted/remaining) and that it's free to call.

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 are no parameters, so schema coverage is 100%. The description adds no parameter info, but none is needed. Baseline 4 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 returns plan and credit usage (used/granted/remaining) for the presented API key. The verb 'get' and resource 'usage' are unambiguous, and it distinguishes well from siblings like list_sources and query_* tools.

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 explicitly says 'Free to call,' providing a usage hint. While it doesn't specify when not to use or alternatives, the tool's simplicity (no parameters) makes the context clear.

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, the description carries the burden of disclosing behavioral traits. It states the tool is free, which implies no side effects or costs, but does not mention authentication requirements, rate limits, or idempotency. The simple nature of the tool (listing data) partly mitigates this, but more detail could be added.

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 long, efficiently conveying the tool's purpose and a key attribute (free). It is front-loaded with the primary function, with no superfluous content.

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 simplicity (no parameters, no output schema), the description adequately explains what the tool returns (datasets with tool names and filter params). It provides sufficient context for an agent to understand its role in discovering available data sources before using other tools.

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 has no parameters, so the description does not need to add parameter information. Schema coverage is complete (100% of zero parameters), so a baseline score of 3 is appropriate. No additional semantics are necessary.

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 tool lists datasets served by the API, along with the tool name and filter parameters for each. It distinguishes itself from sibling tools (get_usage, query_uk_planning, query_uk_tenders) which are specific queries, making its purpose as a discovery tool clear.

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 notes that the tool is 'Free to call,' implying no cost or restrictions. While it does not explicitly say when to use it versus alternatives, the context of sibling tools suggests it should be called first to discover available datasets before querying. However, no exclusions or prerequisite conditions are provided.

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?

Without annotations, the description provides moderate transparency: credit cost, Blind Mode restriction, and filter combination logic. However, it omits details like data freshness, pagination specifics, or error handling, leaving gaps for an agent.

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?

Description is concise (3 sentences) and front-loaded with purpose. It efficiently covers credit cost, filter logic, and Blind Mode, with minimal 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?

Despite 16 parameters and no output schema, the description provides no information on return fields, pagination handling beyond schema, or error codes. This is insufficient for an agent to fully understand tool behavior without external documentation.

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 0% schema description coverage, the description must compensate but only vaguely references 'buyer, country, CPV codes, values, and deadlines.' It explains 'q' searches all fields but does not describe individual parameters (e.g., value_amount_min, deadline_at_after), leaving agents to infer from names 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 that the tool queries EU procurement notices from TED, specifying key fields (buyer, country, CPV codes, values, deadlines) and scope (all member states). The title and sibling tool names (e.g., query_uk_tenders) help distinguish it from UK-specific tools.

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 implies usage for EU-wide procurement queries but does not explicitly state when to use or avoid this tool. It mentions filtering behavior (AND logic, q searches all text) but lacks guidance on alternatives or non-usage scenarios.

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, the description discloses key behaviors: data is normalized, addresses and identifiers are dropped at ingest, and it serves compliance purposes. It also notes the credit cost per call. This provides substantial transparency for a query tool.

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 three sentences, efficiently front-loading the main purpose then adding filter logic and cost. It is concise without being vague, but could be 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 15 parameters and no schema descriptions, the description covers core data and filter logic but lacks parameter-level details and pagination guidance. It provides essential context but is not fully comprehensive.

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 0% for 15 parameters. The description only explicitly addresses the 'q' parameter (searches all text fields) and implies filter behavior. It does not describe individual parameters like name, uei_sam, or date fields, leaving the agent to infer from parameter names.

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 tool queries active US federal exclusions from SAM.gov, listing individuals, firms, etc., and includes details like agency, program, and dates. It distinguishes from sibling tools like query_uk_sanctions by referencing the US government and SAM.gov.

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 that filters combine with AND and that the 'q' parameter searches all text fields, providing clear usage guidance. It also mentions credit cost. However, it does not explicitly discuss when to use this tool versus alternatives, though sibling names imply different jurisdictions.

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 covers key behaviors: data refresh frequency (daily), data exclusion policy (Blind Mode), credit cost (1 credit per call). It provides reasonable transparency for a read-only query tool.

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 with two sentences focusing on key information. It avoids redundancy but could be slightly more structured (e.g., listing parameters).

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 12 parameters, 0% schema coverage, no output schema, and no annotations, the description omits detailed parameter semantics and output structure. It covers the tool's purpose and key constraints but is inadequate for full autonomous invocation.

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 0%, yet the description only explains 'q' and filter behavior generally. It does not describe other parameters like page, per_page, status, company, locality, postal_code, company_type, company_number, incorporated_on_after/before, leaving the agent to infer meaning from names 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 explicitly states it retrieves the newest UK companies from Companies House API, listing specific fields (name, number, type, SIC codes, incorporation date, area). This clearly differentiates from sibling tools like query_uk_insolvency or query_uk_sanctions.

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 Blind Mode (officer/PSC data not ingested, addresses stripped to locality/postcode) and notes that filters combine with AND and q searches all text fields. It doesn't explicitly state when not to use, but the context is sufficient for an agent to decide.

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, the description carries the burden of behavioral disclosure. It reveals important traits: Blind Mode (person fields and notice text not ingested), cost per call, and that only company-level facts are returned. However, it does not describe pagination behavior or error handling, and there is no output schema to clarify response structure.

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 three sentences long, with no superfluous information. The first sentence front-loads the primary purpose and scope, the second addresses a key privacy behavior, and the third covers filtering logic and cost. Every 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 the tool has 8 parameters, no output schema, and no annotations, the description provides a reasonable overview but leaves gaps. Key parameters like notice_code and notice_type are undefined, and the response format is not described. The 'company-level facts only' limit is helpful, but the completeness is insufficient for a parameter-rich tool with zero schema descriptions.

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 0%, so the description must compensate. It only adds meaning for the 'q' parameter (searches all text fields). The other seven parameters (page, company, notice_code, etc.) receive no explanation. The date parameters have regex patterns but no human-readable semantics, leaving the user to infer valid values.

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: retrieving the latest corporate insolvency notices from The Gazette. It specifies the types of notices included (winding-up petitions, appointments, etc.) and distinguishes it from sibling tools like query_uk_companies by focusing on insolvency data. The mention of 'Blind Mode' adds further clarity.

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 how to use filters (AND combination, q searches all text) and mentions cost per call, but does not explicitly state when to use this tool versus alternatives such as query_uk_companies or query_uk_tenders. The purpose implies usage for corporate insolvency, but no direct guidance on exclusions or alternatives.

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 discloses 'Blind Mode: no applicant personal data' and cost per call, but does not mention read-only nature, authentication, error handling, or response format.

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 efficient sentences front-loading purpose, with no wasted words. Every 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 parameters, no output schema, and no annotations, the description fails to explain pagination, output structure, or parameter details beyond q, leaving significant gaps for agent usage.

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 0% schema coverage, description should compensate, but it only adds meaning to the 'q' parameter ('searches all text fields'). Other parameters (page, per_page, authority, reference, decision_date) are left undefined.

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 queries planning applications from the official planning.data.gov.uk feed, normalized to one schema. It distinguishes from siblings like query_uk_tenders (for tenders) by domain.

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 filters combine with AND and q searches all text fields, but does not explicitly state when to use this tool over alternatives or provide exclusion criteria.

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, the description carries the full burden. It discloses that dates of birth, identity documents, and contact details are dropped at ingest, and notes the credit cost. This provides useful behavioral context beyond a simple query tool, though it could mention pagination behavior or error handling.

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 three sentences long, front-loaded with the core purpose, and contains no redundant information. Every sentence adds value, making it efficient for an agent to parse.

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 explains data scope, filters, and credit cost, but does not mention pagination limits (page and per_page parameters exist), response structure, or error handling. Given 13 parameters and no output schema, it is adequate but not fully complete.

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 0% for 13 parameters. The description adds that 'q searches all text fields' and filters combine with AND, but does not explain other parameters like 'name', 'regime', 'countries', 'unique_id', or the date fields. Given the high parameter count, the description should provide more semantic detail.

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 queries the official UK Sanctions List for individuals, entities, and ships, with specific fields like regimes, aliases, and dates. This verb+resource combination is specific and distinguishes it from sibling tools like query_uk_planning or query_uk_tenders.

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 implies usage for supplier due diligence and mentions that filters combine with AND. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide when-not-to-use scenarios. The context is clear but lacks explicit guidance.

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, the description adds useful behavioral context: Blind Mode (no personal data), cost of 1 credit per call, and data source. It does not cover rate limits, pagination, or error responses, but overall adequate.

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 sentences are concise and well-structured, with purpose first, followed by key features and cost. No unnecessary 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?

Despite 11 parameters, no output schema, and no annotations, the description provides minimal guidance on parameter format, allowed values, or return structure. It fails to cover common usage scenarios.

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 0%, so the description must compensate. It only explains that q searches all text fields and filters combine with AND, leaving the semantics of 11 parameters (e.g., buyer, cpv_codes) unexplained.

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 identifies the tool's purpose: querying UK procurement notices from the Find a Tender OCDS feed. It distinguishes from sibling query_uk_planning by specifying the official feed and domain (tenders vs planning), though not explicitly.

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 states that filters combine with AND and q searches all text fields, providing basic usage context. However, it does not specify when to use this tool over siblings like query_uk_planning or when not to use it.

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