ENTIA Entity Verification MCP

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

Annotations already establish readOnly, idempotent, openWorld, and non-destructive behavior. The description adds valuable context about auth-dependent response tiers: free callers get a trace-level preview while authenticated callers get the full paid dossier. This is useful behavior beyond what annotations provide and contains 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 concise sentences with no wasted words. The core purpose is front-loaded, and the auth-tier distinction is stated efficiently.

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 an output schema present and annotations covering safety semantics, the description adequately supports invocation. It covers scope, input types, and auth-dependent behavior. The only gap is the lack of guidance on how this tool relates to similar siblings.

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%, with all three parameters already well-documented in the schema. The description does not need to repeat parameter details, but it also adds no additional semantic nuance beyond what the schema already states.

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?

States a clear verb and object ('Look up any business entity') and enumerates accepted identifiers (name, CIF/NIF, EU VAT, LEI). It distinguishes itself from a generic search tool by emphasizing direct identifier-based lookup, though it does not explicitly contrast with siblings like search_entities or get_full_dossier.

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 this is for looking up a specific entity when you already have an identifier or exact name, but it does not state when to use this instead of siblings like search_entities, professional_lookup, or get_full_dossier. No exclusions or alternative routing 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?

Annotations already indicate the tool is read-only, open-world, idempotent, and nondestructive. The description adds behavioral insight by explaining how sector aliases are resolved (e.g., `abogados` resolves to canonical slug) and that the response includes `_meta.sector_resolved_from`. This goes beyond annotations, though it does not detail rate limits or potential response variability.

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 (3 sentences) and front-loaded with the core purpose. The additional detail about sector vocabulary is placed naturally, but the last sentence could be slightly more streamlined to avoid over-explaining the alias resolution, though it remains valuable.

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 an output schema and clear annotations, the description covers the key aspects: purpose, sector vocabulary behavior, and geographic scope. It does not address pagination or ordering of results, which could be inferred from the `limit` parameter. For a ranking tool with clear constraints, this is nearly complete but missing an explicit statement about result ordering (e.g., by relevance).

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%, but the description adds significant value beyond the schema by explaining that `sector` is a closed vocabulary with aliases that resolve to canonical slugs, and that the response will indicate the resolution. It also clarifies that `city` is limited to Spanish cities, reinforcing the geographic constraint. This rich semantic guidance helps the agent avoid errors with non-canonical 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: 'Find real competitors in the same sector and city.' It specifies the criteria (identity, location, sector matching) and geographic scope (Spain only), which distinguishes it from siblings like `search_entities` or `entity_lookup` that likely have broader or different search capabilities.

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 usage guidance by specifying the geographic constraint ('Spain only on the public surface') and noting that the `sector` parameter uses a closed vocabulary with aliases. However, it does not explicitly mention when not to use this tool (e.g., for non-Spain competitors or non-public data) or compare it directly to sibling tools, though the sibling context suggests differentiation from `search_entities`.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is well-covered. The description adds value by stating 'Fail-closed: never returns ENTIA corporate marketing as if it were the entity', which is a critical behavioral promise beyond annotations. It also clarifies the exact URL path structure, aiding the agent in understanding the resource location.

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, each serving a distinct purpose: first sentence states the action and resource, second provides parameter usage guidance referencing sibling tools, third gives a behavioral constraint. No wasted words, 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?

Given the high schema coverage, rich annotations (read-only, idempotent, open-world), presence of an output schema, and 4 clear path parameters, the description is complete. It explains the resource URL pattern, how to get the required path, and a safety constraint. No obvious gaps for an agent to invoke this tool 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%, so the baseline is 3. The description adds meaning by explaining how the four path parameters (country, sector, city, slug) combine into the JSON-LD URL pattern, and contrasts them with 'cif/q' parameters used by other tools. This provides semantic coherence beyond individual field descriptions in 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 that the tool retrieves a Schema.org JSON-LD @graph for a specific entity's Entia Home. It specifies the URL pattern (/v1/identity/{country}/{sector}/{city}/{slug}.jsonld) and distinguishes it from other tools by noting it uses country/sector/city/slug path segments, not cif/q. This differentiates it well from siblings like entity_lookup 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 explicitly states how to obtain the required path via entity_lookup/showcase (canonical_url), providing clear guidance on when to use this tool. It does not explicitly mention when not to use it or name alternatives for exclusion, but the context signals and sibling list imply alternatives are available. The fail-closed warning about not returning corporate marketing adds practical behavioral 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?

Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds valuable behavioral context: it reads from a materializer snapshot, is served from R2, and has no enrichment. This goes beyond annotations without contradicting them.

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 with no waste. First sentence states purpose, second states equivalence, third states sourcing and limitation. It is front-loaded and 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 presence of an output schema and rich annotations, the description covers the key aspects: what data is returned (claims+projection+policy), data source (materializer snapshot), equivalence to public ficha, and the render_context option. It is sufficiently complete for a read-only projection 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 description coverage is 100%, so baseline is 3. The description adds no parameter-specific information; all parameters are fully documented in the schema. The description does not enhance understanding of parameter semantics.

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 reads a materializer snapshot for an Entia Home containing claims, projection, and policy. It specifies it is the same truth as the public ficha HTML/JSON-LD twin and served from a specific R2 file. This specific verb+resource+scope distinguishes it from siblings like get_entia_home and get_full_dossier.

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 by noting equivalence to the public ficha and that it lacks ad-hoc enrichment, but does not explicitly state when to use this tool versus alternatives like get_entia_home or get_full_dossier. The context is provided but no direct when-not or exclusions 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?

Annotations already cover the safety profile (read-only, idempotent, non-destructive). The description adds useful context about parallelism and multi-source aggregation, but does not disclose potential latencies, failure semantics, or whether partial source failures affect the result. This is acceptable but not rich.

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 core value proposition. The 'Killer tool' phrasing is informal and adds little factual value, but the overall length is well suited to the tool's 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?

Given an output schema exists and the only input is fully documented, the description covers the essential context: aggregation of sources, field volume, and intended use case. It is slightly thin on alternative routing and behavioral caveats, but nothing critical is missing for an agent 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?

The schema already fully documents the single query parameter, including accepted formats (Company name, CIF/NIF, EU VAT, LEI). The description adds no additional parameter-level guidance beyond referring to 'an entity,' so a 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 this is an aggregator that returns 90+ fields about an entity by combining four named ENTIA sources. It distinguishes itself from simpler lookup tools through the aggregator framing and 'due diligence/KYB' positioning, though it does not name a specific sibling alternative.

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 communicates a clear target use case — 'due diligence/KYB' — and implies a comprehensive one-call need. However, it does not explicitly state when not to use it, what simpler tools to prefer for narrower queries, or where it fits relative to entity_lookup, search_entities, or verify_vat.

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, openWorldHint, idempotentHint, and destructiveHint=false, covering safety and idempotency. The description adds 'Live' to indicate real-time data, which is useful but not critical. No additional behavioral traits (e.g., rate limits, caching) are disclosed.

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, front-loaded sentence that efficiently communicates the tool's purpose and output. Every word adds value, and there is no repetition or irrelevant detail.

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 zero-parameter tool with rich annotations and an output schema, the description is largely complete. It specifies the nature ('Live') and the data categories. However, it could briefly mention that the output is a structured object matching the schema, or clarify whether 'Live' implies real-time vs. cached.

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?

No parameters exist, so schema coverage is 100%. The description does not need to explain parameters. The baseline of 4 is appropriate since the tool requires no input and the description lists the output fields.

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 'Live platform stats' and lists specific metrics: entities count, countries, sources, homes published. The verb 'get' combined with resource 'platform stats' is unambiguous and distinct from siblings like entity_lookup or get_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?

No guidance on when to use this tool vs alternatives. The sibling list includes other stats-like tools (e.g., get_entia_home, get_showcase), but the description offers no context for differentiation, such as scope or use case.

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, openWorldHint, idempotentHint, and non-destructive, so the safety profile is fully covered. The description adds a meaningful behavioral detail beyond annotations: 'FREE — does not consume quota,' which affects invocation decisions. It doesn't contradict the annotations and no destructive behavior is implied.

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 short sentences totaling about 15 words: purpose, cost, and usage intent. Every sentence earns its place with no repetition of schema field names or annotation details. This is an exemplary concise tool description.

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 low-complexity tool (2 optional params, no required fields, output schema present), the description covers the key operational facts: curated sample scope, free/quota behavior, and intended use case. Missing elaboration on what 'data depth' means or the exact return shape, but the output schema handles return details.

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%, with inline parameter descriptions for limit (default 3, max 5) and category (enum with default 'all'). The description's mention of 'IBEX35 + EU' loosely maps to the category enum but adds no new syntax, defaults, or format details beyond what the schema already supplies. 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 opens with 'Curated IBEX35 + EU entity examples,' naming the resource and the curated/sample nature, so the agent knows it returns sample entities. The title 'Get Showcase' and the FREE/explore wording position it as an exploratory, read-only endpoint. It is distinct from siblings like entity_lookup or run_risk_audit, though it doesn't explicitly name a sibling to distinguish itself from.

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 instruction 'Use to explore data depth' gives a clear intended-use context, telling the agent this is for data exploration rather than lookup or auditing. It doesn't provide explicit alternatives or when-not-to-use conditions, but given the sibling list this is clearly the demo/example tool. This meets the 'clear context, no exclusions' level.

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, openWorldHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds scope context ('24 Spanish health/legal/psychology verticals') and lists returned fields, but does not disclose additional behavioral traits such as external source behavior, rate limits, or missing-result 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 two short sentences: the first states the tool's core purpose, and the second lists the key return fields. It is front-loaded, free of filler, and every sentence adds useful 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?

For a simple two-parameter, read-only lookup with complete schema descriptions and an output schema, the description is largely sufficient. It could add a brief explicit pointer to sibling tools or an example, but nothing essential is missing for selecting and invoking 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%, with query described as 'Professional name, colegiado number, or REPS identifier' and vertical as 'Healthcare/legal vertical (dental, medicos, psicologia, ...)'. The tool description itself adds little parameter-level meaning beyond naming the vertical domains, so a 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 opens with a specific action and resource: 'Verify professional registrations' across 24 named Spanish health/legal/psychology verticals. It clearly separates this tool from generic siblings like entity_lookup or search_entities, and the listed return fields make the purpose concrete.

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 clear context: use it to verify professional registration data across Spanish verticals, and it names the vertical categories. It does not explicitly mention when not to use it or point to a sibling alternative, so it stops short of a 5.

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 provide readOnlyHint, idempotentHint, and destructiveHint, so the agent knows the tool is safe and idempotent. The description adds valuable behavioral details: the rate limit (5 req/min), timeout (30s), and the specific analysis areas (SSL, DNS, etc.). This goes beyond what annotations offer, though it does not describe error handling or edge cases.

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 covering purpose, analysis scope, output, and constraints. Every sentence adds value, and the most critical information (what the tool does) is front-loaded. No redundant or unnecessary 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 the tool's moderate complexity (multiple analysis areas), the presence of detailed annotations, 100% parameter schema coverage, and an output schema, the description is complete. It covers the core function, key inputs, behavioral constraints, and return value without needing to enumerate all possible outputs or error states.

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 three parameters are described in the input schema (100% coverage), so the schema already provides meaning for each parameter. The description reiterates the domain focus but does not add new semantics beyond the schema. With high schema coverage, the baseline is 3, and the description does not elevate it further.

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: 'Run comprehensive AI-readiness + digital risk audit on any domain.' It specifies the verb (run audit), the resource (domain), and the analysis areas (SSL, DNS, structured data, LLM visibility). It also mentions the return value (risk score 0-100), making it distinct from sibling tools like entity_lookup or get_competitors.

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 implicitly tells the agent to use this tool when a risk audit is needed, but it does not explicitly contrast with sibling tools or provide when-not-to-use guidance. There is no mention of alternatives or exclusion criteria, leaving the agent to infer the use case from the tool's name and description alone.

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 meaningful behavioral context: anonymous vs. authenticated access levels affect result richness, and sector aliases resolve to canonical slugs with a _meta field. This goes beyond the annotations, though it does not disclose rate limits or pagination 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 three sentences that front-load the core purpose, include examples, and cover key behavioral nuances. Every sentence earns its place; there is no redundancy or fluff.

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 (5 parameters, 56-sector enum, output schema exists), the description covers the most critical aspects: auth-level differences, alias resolution, and usage examples. However, it does not clarify which 10 countries or explain the city/country filtering behavior beyond the examples, leaving minor 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%, so the baseline is 3. The description adds value by explaining that the sector parameter uses a closed vocabulary with alias resolution, that aliases resolve to canonical slugs, and that the response includes _meta.sector_resolved_from. This extra context about parameter behavior goes beyond the schema's enum listing.

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 resource ('verified entities across 10 countries') and action ('search'), with specific filters (name, keyword, country, city, sector) and example queries. It is specific and actionable, but does not explicitly distinguish from sibling tools like entity_lookup or get_competitors, which would justify a 5.

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 usage examples ('find me a dentist in Madrid') and notes that the sector parameter has a closed vocabulary with alias resolution. However, it lacks explicit guidance on when not to use this tool or which alternatives (e.g., entity_lookup for exact lookups) are more appropriate, leaving usage implied rather than directed.

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 indicate readOnlyHint, idempotentHint, and non-destructive behavior. The description adds value by stating it's real-time via VIES and detailing the return fields: {valid, name, address, vat_number, country}. No contradictions 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 a single, front-loaded sentence with no filler. Every word contributes: nature (real-time), source (VIES), scope (27 countries), and return structure. It is optimally concise for the agent.

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 lookup tool with one parameter family, the description covers the core functionality and output fields. The presence of an output schema further complements completeness. No gaps are evident given the tool's complexity.

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%; each parameter already has a description. The tool description does not add extra meaning beyond the schema, but the brief note on return format indirectly hints at how the parameter affects results. Baseline 3 is appropriate given full 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 the tool performs 'Real-time EU VAT validation via VIES (27 countries)', specifying the verb (validate) and resource (EU VAT numbers). It distinctly separates this from sibling tools like entity_lookup or get_competitors, 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?

The description implies usage when needing to validate an EU VAT number, but provides no explicit guidance on when not to use it, prerequisites, or alternatives among siblings. All parameters are optional in schema, yet logically one must be provided—the description does not clarify this constraint.

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 annotations already declare this tool as readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description largely reinforces the read-only nature by describing it as a 'profile', but it does not add behavioral context beyond the annotations. For example, it doesn't mention the size of the response (the output schema exists), or any potential limits on calls, which would add value.

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, tightly packed sentence that communicates the core function and scope. It efficiently lists the key data domains without unnecessary elaboration. Every part of the sentence earns its place, and the information is front-loaded with the primary verb 'Socioeconomic profile'.

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 a single required parameter with full schema coverage and an output schema exists, the description is minimally adequate. It explains what the tool does and what you need to provide. However, for a tool that outputs 17 blocks of data, it could be more helpful by briefly summarizing a few typical use cases or clarifying the breadth of each block (e.g., 'real estate' could mean house prices vs. construction permits). The output schema might cover this, but the description doesn't lean into it.

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 has 100% description coverage for the single parameter 'postal_code', including the pattern and an example. The description adds context by stating the tool is for Spanish codes, which reinforces the schema's Spanish designation. Since coverage is high, a baseline of 3 is justified; the description adds a concrete example, pushing it to a 4.

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 socioeconomic profiles for Spanish postal codes, listing 17 specific data blocks (income, employment, etc.). This gives a strong and specific sense of the tool's purpose, though it doesn't explicitly differentiate it from its siblings, particularly 'entity_lookup' or 'get_full_dossier', which might have overlapping geographic data.

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 implicitly defines usage context: when you need socioeconomic data for a Spanish postal code. It includes a realistic example (28013 = Madrid Gran Vía) that aids selection. However, there is no explicit guidance on when *not* to use it (e.g., if you need a broader region) or alternatives among the listed siblings, which would have earned a 5.

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