ENTIA Entity Verification

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

No annotations are provided, so the description must convey behavioral traits. It describes the output structure (JSON-LD, 4-node graph) and implies a read-only lookup, but does not disclose potential side effects, authentication needs, rate limits, or error states. The output format detail adds some 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 concise (two sentences), front-loading the essential output structure. It efficiently conveys the key information without verbosity, though it could expand slightly on usage guidance without becoming wordy.

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 partially compensates by listing the 4-node graph components. However, it lacks details on error handling, response size limits, or disambiguation from similar tools like get_full_dossier. It is adequate for a simple tool but 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?

With 100% schema coverage, the input schema already documents the sole parameter 'query' as 'Company name or domain.' The description adds context about the output but does not enhance parameter semantics beyond the schema's description. Baseline 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 specifies a 'Full AI-ready JSON-LD profile' and details a 4-node @graph structure (Organization, Place, LocalBusiness, PostalAddress), clearly distinguishing it from sibling tools that retrieve different entity data or formats. The purpose is direct and 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?

The description does not explicitly state when to use this tool over alternatives like get_full_dossier or entity_lookup. It mentions 'Designed for direct AI citation' as a use case, but lacks comparative guidance or exclusion criteria for other tools.

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; description carries the full burden. It discloses key behavioral trait: response differs based on authentication (trace-level preview vs full dossier). This is valuable transparency about the tool's core behavior.

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, zero wasted words. Front-loaded with purpose and supported by authentication-dependent detail. Highly 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?

Tool has 3 parameters with 0 required, and no output schema. Description does not clarify behavior when no parameters are provided, nor does it describe return values or potential errors. Lacks completeness for a lookup tool with flexible input.

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%; all three parameters have descriptions in the schema. The tool description merely reiterates the search types already in the schema, adding no new semantic value beyond what the schema provides.

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 uses strong verb 'look up' and specifies resource 'business entity', with clear identifiers (name, CIF/NIF, EU VAT, LEI). It distinguishes from siblings like 'get_full_dossier' by mentioning preview vs full dossier based on authentication.

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?

Description gives context on outcome based on authentication status (free vs paid), but does not explicitly state when to use this tool vs alternatives like 'search_entities' or 'ai_ready_profile'. No 'when not to use' guidance is 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?

No annotations provided; description carries full burden. It mentions 'ranked entities' but does not explain how ranking works, what happens with no results, or other behavioral traits. Insufficient 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?

Two sentences, front-loaded with purpose and context. Every sentence adds value, no fluff. Highly concise and well-structured.

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?

Tool has 3 params, no output schema, no annotations. Description does not explain output format or ranking details. Incomplete for an agent to understand expected results or edge cases.

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 67%, with descriptions for city and sector. The description does not add meaning beyond schema; the 'limit' parameter lacks description in both schema and description. Minimal enhancement.

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 finds competitors by sector and geography, with ranked entities. It uses specific verbs and resources, distinguishing it from siblings like entity_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?

The description implies when to use (find competitors in same sector/geography) but provides no explicit guidance on when not to use or alternatives. No exclusions or context vs siblings.

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 adds useful behavioral context: it is 'fail-closed' and never returns ENTIA corporate marketing as if it were the entity. This helps the agent understand reliability and edge cases. Additional details on failure modes or rate limits would improve 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 concise yet dense with information. Every sentence serves a purpose: defining the action, specifying the URL pattern, clarifying parameter sources, and noting fail-closed behavior. No redundant text.

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 adequately explains what the tool returns (Schema.org JSON-LD @graph) and how the request path is constructed. It also covers a key behavioral detail. However, it could mention what happens if the entity does not exist or if the path is invalid.

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?

Although the input schema already provides descriptions for all four parameters (100% coverage), the description adds value by clarifying that these are URL path segments and explaining how to obtain them via entity_lookup or showcase. This goes beyond the schema's basic type/pattern constraints.

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 the Schema.org JSON-LD @graph for a specific entity's Entia Home, specifying the URL path pattern. It distinguishes from other tools by noting that it does not use 'cif/q' parameters and that the path must be obtained via entity_lookup/showcase.

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 gives guidance on prerequisites: requires path segments and directs users to obtain the path via entity_lookup or showcase. However, it doesn't explicitly state when not to use this tool or list alternatives among the sibling tools.

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 exist, so the description carries full burden. It correctly identifies the tool as a read operation and states the data source and lack of enrichment. However, it does not disclose error behavior, freshness, or any potential side effects beyond being read-only.

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, front-loaded with the action, and every sentence adds value. No redundant or extraneous 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?

Given no output schema and moderate complexity (5 params, 4 required), the description explains what the tool returns (claims, projection, policy) and its equivalence to public data. It could mention behavior on not found or stale data, but is largely complete for a read 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?

Schema coverage is 100%, so each parameter is described in the schema. The tool description adds no additional meaning to the parameters beyond what the schema provides, so 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?

The description clearly states it reads a specific materializer snapshot for an Entia Home, listing contents (claims + projection + policy) and noting equivalence to public ficha. It is specific and gives a clear sense of what the tool retrieves, but does not explicitly differentiate from siblings like 'get_entia_home'.

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 mentions it serves from an R2 sibling with no ad-hoc enrichment, implying a cached projection, but provides no guidance on when to use this tool versus alternatives like 'get_entia_home' or 'get_full_dossier'. No exclusions or context are given.

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 full burden. It mentions parallel source combination (non-destructive, read-only) but omits details on idempotency, error behavior, or authentication needs. The description is adequate but not thorough.

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, front-loaded with purpose and immediately providing context. Every word earns its place; no 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?

For a complex aggregator with no output schema, the description lacks details about output structure, error handling, or field examples. While the single parameter is well-covered, the overall context is not fully complete for an AI agent to anticipate results.

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% and describes the query parameter as 'Company name, CIF/NIF, EU VAT, or LEI'. The description adds no additional parameter meaning beyond what the schema provides, meeting the baseline but not exceeding it.

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 is an aggregator that returns 90+ fields from 4 ENTIA sources, specifically for due diligence and KYB. This distinguishes it from siblings like entity_lookup or zone_profile, which likely have narrower scope.

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 use for comprehensive due diligence but does not explicitly instruct when to use versus alternatives or provide exclusions. It is clear enough for context 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?

No annotations are provided, so the description must fully disclose behavioral traits. It states the data is 'live' but does not mention read-only nature, authentication requirements, rate limits, or any side effects. The minimal description leaves significant gaps.

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 concise sentence that delivers the core purpose without extraneous words. It is front-loaded with the key information, though it could be slightly more structured.

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 zero parameters and no output schema, the description suffices for a simple stats tool by listing returned elements. However, it omits details like pagination, authentication, or response format, leaving the agent with an incomplete understanding.

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 zero parameters, baseline is 4. The description adds value by naming the kinds of data returned (entities count, countries, sources, homes published), which meaningfully supplements the empty schema.

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 retrieves 'Live platform stats' and lists specific data elements (entities count, countries, sources, homes published). It effectively communicates the resource and scope, but does not differentiate from sibling tools such as 'get_entia_home' or 'get_entity_home_projection'.

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 no guidance on when to use this tool versus alternatives. There is no mention of prerequisites, context, or any 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?

With no annotations, the description bears full responsibility for behavioral disclosure. It states the tool is free and does not consume quota, which is helpful. However, it does not describe the output format or structure, leaving agents uncertain about the return data.

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 consists of two short, front-loaded sentences. The first defines the tool's content and the second adds key behavioral context. No unnecessary words are present.

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 conveys the purpose and key traits. However, lacking output format details, the agent may not fully understand what to expect from the return data.

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 tool has zero parameters and the schema coverage is trivially 100%. The description correctly reflects this condition and adds no misleading information. There is no need for further parameter 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 returns curated examples of IBEX35 and EU entities, and specifies it is free. The verb 'get' and resource 'showcase' are implicit. It distinguishes from siblings like entity_lookup or search_entities by focusing on examples, but does not explicitly list the output fields.

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 suggests using the tool 'to explore data depth' and notes it is free, which encourages usage over paid alternatives. However, it does not provide explicit when-to-use or when-not-to-use criteria, nor does it mention alternative tools for specific tasks.

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 must disclose behavioral traits. It only states 'verify', implying read-only, but no details on authentication, rate limits, or failure behavior.

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 with a follow-up summary, directly stating the action and output, with 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?

The description covers input types and output fields, which is adequate for a simple lookup tool. However, it lacks details on error handling or result limits, and no output schema is provided.

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% for the single parameter, which already describes acceptable inputs (name, colegiado number, REPS identifier). The tool description adds no further parameter-level meaning beyond the schema.

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 verifies professional registrations across 24 Spanish verticals and specifies the return fields (colegiado number, college, specialty, status), distinguishing it from sibling tools like entity_lookup or search_entities.

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 use for verifying professional registrations but provides no explicit guidance on when to use this tool versus alternatives, nor 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.

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

The description discloses rate limits (5 req/min) and timeout (30s), and states the output is a risk score. However, it does not specify if the tool is read-only, authentication requirements, or error handling, which is notable given no annotations are provided.

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 extremely concise (two sentences) and front-loaded with the primary action. Every sentence adds value and there is no 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?

Without an output schema, the description only mentions a risk score but does not detail the full return structure. It adequately covers the basic purpose and constraints but lacks depth for a comprehensive audit 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?

Schema coverage is 100% as all parameters have descriptions. The description adds no extra meaning beyond the schema; it only refers to 'domain' implicitly. 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 the tool conducts an AI-readiness and digital risk audit on a domain, listing analysis areas (SSL, DNS, structured data, LLM visibility) and a risk score. However, it does not differentiate from sibling tools like 'ai_ready_profile', which may have overlapping functionality.

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?

No guidance is provided on when to use this tool versus alternatives like 'ai_ready_profile' or 'entity_lookup'. There are no prerequisites, context suggestions, or exclusions mentioned.

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 key behavioral traits: two access tiers (anonymous preview vs. authenticated full records). This adds context beyond the schema, though it omits details like rate limits, pagination, or ordering.

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 concise sentences, front-loaded with core purpose and a second sentence for behavioral nuance. Every word earns 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?

Given the absence of an output schema and annotations, the description covers core use and access behavior but leaves gaps: no mention of return structure, pagination, or what 'full records' contain. 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?

The input schema already has 100% coverage with detailed descriptions for each parameter. The description reiterates the parameter roles (name, keyword, country, sector) but does not add new meaning beyond what the schema provides.

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 verb (search), resource (verified entities), and scope (across 10 countries by name, keyword, country, or sector). It effectively distinguishes from sibling tools like entity_lookup, which likely targets specific entities rather than broad search.

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 the tool is for general search across countries and notes access-level differences (anonymous vs. authenticated), but it does not explicitly state when to use this tool versus alternative sibling tools such as entity_lookup or get_full_dossier.

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 the real-time nature and the use of VIES, and specifies the return structure. However, with no annotations, it lacks details on idempotency, required permissions, rate limits, or behavior for invalid VAT numbers.

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: first states functionality and source, second specifies return format. Every word contributes, no 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 return format is well-described, but the description omits guidance on parameter selection (e.g., which parameter is preferred) and does not address error conditions or prerequisites. With three optional parameters but logically one required, more context would be helpful.

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 baseline 3 applies. The description does not add additional meaning to the parameters beyond what the schema already provides (aliases and examples).

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 validates EU VAT numbers in real-time via VIES, covering 27 countries, and explicitly lists the return fields. This distinguishes it from sibling tools like entity_lookup or search_entities which serve different purposes.

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?

No guidance is provided on when to use this tool versus alternatives, nor any conditions where it should not be used. Sibling tools exist but no comparative information is given.

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 must disclose behavioral traits but does not. It does not mention if the tool is read-only, whether data is real-time or cached, if authentication is needed, or any constraints. The list of blocks is helpful but insufficient for full 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 a single sentence that efficiently conveys the tool's purpose and scope. It is front-loaded and avoids unnecessary words, earning its place with no 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?

For a simple single-parameter tool without output schema, the description adequately covers the input and high-level output. However, it omits details about the structure of the profile (e.g., data format, time periods) that would help an agent understand the return value, leaving some ambiguity.

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 detailed parameter description including pattern and example. The description adds no extra semantic value beyond the schema, meeting the baseline expectation but not exceeding it.

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 provides a socioeconomic profile for a Spanish postal code, listing 17 specific data blocks. It distinguishes from sibling tools by specifying the geographical scope (Spanish postal codes) and the breadth of data included, making its purpose very 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?

No guidance is given on when to use this tool versus alternatives like 'ai_ready_profile' or 'get_full_dossier'. There is no mention of prerequisites, use cases, or when not to use it, leaving the agent to infer usage from the purpose alone.

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