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 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.
Add one secure layer between your agents and this server.
Most tools target distinct purposes: entity lookup vs. full dossier vs. VAT verification vs. professional lookup are clearly different. However, get_entia_home and get_entity_home_projection are very similar (both about Entia Home) and could cause confusion, though descriptions clarify the difference. Overall, overlaps are minimal and well-described.
The naming mixes conventions: 'get_' prefix is used for 8 tools, but others like 'entity_lookup', 'professional_lookup', 'zone_profile' use noun phrases without a verb. 'run_risk_audit' and 'verify_vat' are verb-based but follow different patterns. The inconsistency could confuse an agent expecting a uniform verb_noun structure.
With 12 tools, the server covers a broad but well-scoped domain of entity verification and business intelligence. Each tool serves a clear purpose, and the count is neither too sparse nor overwhelming for the intended functionality.
The tool surface covers core entity lookup, search, dossier, VAT, professional verification, and even added value like risk audit and zone profiling. Minor gaps exist (e.g., no tool for updating entity data), but for a read-heavy verification service, the set is comprehensive and leaves few dead ends.