professional_lookupVerify professional registrations across 24 Spanish health/legal/psychology verticals. Returns colegiado number, college, specialty, status.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Professional name, colegiado number, or REPS identifier | |
| vertical | No | Healthcare/legal vertical (dental, medicos, psicologia, ...) |
| Name | Required | Description | Default |
|---|---|---|---|
| _meta | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare this as read-only, open-world, idempotent, and non-destructive. The description adds useful behavior beyond that: it defines the output semantics (colegiado number, college, specialty, status) and scopes the domain to 24 Spanish verticals. There is no contradiction with the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with no filler. The core action is front-loaded, followed by a compact list of return fields. Every word contributes.
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 a 2-parameter schema with 100% coverage, an output schema, and strong safety annotations, this description is complete for an agent to select and invoke the tool correctly. It covers what the tool does, its scope, and what it returns.
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 does not add parameter-specific details beyond what the schema already states, but it does reinforce that the query is for professional identity and that verticals are health/legal/psychology domains. That is adequate but not additive.
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 names a specific verb, 'Verify', and a concrete resource: professional registrations across 24 Spanish health/legal/psychology verticals. It also tells the agent what result to expect (colegiado number, college, specialty, status), which sharply distinguishes it from broader siblings 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 phrase 'Verify professional registrations' makes the intended use case clear: an agent should call this when confirming a professional's credential status. It does not explicitly name alternatives or exclusions, but the verification framing provides a distinct context among siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools target distinct resources, but get_entia_home and get_entity_home_projection are near-duplicates representing the same Entia Home concept from slightly different technical angles. entity_lookup and get_full_dossier also overlap since entity_lookup already mentions returning the full paid dossier, and get_competitors is a specialized variant of search_entities.
The set mixes get_* names with bare action names like entity_lookup, search_entities, verify_vat, and professional_lookup, so there is no single consistent verb_noun pattern. The names are still readable and somewhat predictable, but the inconsistency is noticeable and get_entia_home vs get_entity_home_projection adds confusion.
Twelve tools is within a reasonable range for an entity-verification server, and most tools serve a distinct due-diligence purpose. However, a few tools are auxiliary or redundant: get_showcase and get_platform_stats are exploratory rather than core verification, and the two Entia Home retrieval tools could be consolidated.
The server covers the core verification lifecycle well: entity lookup by identifiers, flexible search, VAT validation, professional verification, competitor discovery, full dossiers, and risk auditing. Minor gaps exist, such as no direct way to fetch a raw single source like VIES or BORME independently and no entity relationship/ownership endpoint, but these are workable for typical KYB workflows.