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ENTIA-IA

ENTIA Entity Verification

Official

entity_lookup

Verify a company’s identity by name or identifier; obtain trust score, jurisdiction, and legal acts across 34 countries.

Instructions

Verify the identity of any business across 34 countries.

Use when: user asks "is this company legit?", "check CIF B80988678", "verify Telefonica". Returns: Trust Score 0-100, BORME acts count, LEI, Wikidata QID, jurisdiction.

Example: entity_lookup("Telefonica")

Args: query: Company name (Telefonica), CIF (A28015865), EU VAT (ESA28015865), or LEI (20 chars)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • The actual implementation of the entity_lookup tool. It is decorated with @mcp.tool(), accepts a query string, and calls the REST API GET /v1/profile/{query} via the _get() helper.
    @mcp.tool()
    def entity_lookup(query: str) -> dict[str, Any]:
        """Verify the identity of any business across 34 countries.
    
        Use when: user asks "is this company legit?", "check CIF B80988678", "verify Telefonica".
        Returns: Trust Score 0-100, BORME acts count, LEI, Wikidata QID, jurisdiction.
    
        Example: entity_lookup("Telefonica")
    
        Args:
            query: Company name (Telefonica), CIF (A28015865), EU VAT (ESA28015865), or LEI (20 chars)
        """
        return _get(f"/v1/profile/{query}")
  • Tool registration via the @mcp.tool() decorator on the FastMCP instance, binding the function as an MCP tool named 'entity_lookup'.
    @mcp.tool()
    def entity_lookup(query: str) -> dict[str, Any]:
  • The _get() helper function used by entity_lookup to make authenticated GET requests to the ENTIA REST API.
    def _get(path: str, params: Optional[dict[str, Any]] = None) -> dict[str, Any]:
        """GET request to ENTIA REST API."""
        url = f"{BASE_URL}{path}"
        try:
            with httpx.Client(timeout=TIMEOUT) as client:
                r = client.get(url, headers=_headers(), params=params or {})
            r.raise_for_status()
            return r.json()
        except httpx.HTTPStatusError as exc:
            return {"error": str(exc), "status_code": exc.response.status_code}
        except Exception as exc:  # noqa: BLE001
            return {"error": str(exc)}
Behavior3/5

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

No annotations are provided, so the description carries full burden. It discloses return fields and accepted input formats, but does not mention safety, authentication needs, or error behavior. It adds some value beyond schema but lacks depth for a no-annotation scenario.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise (6 lines) and well-structured: purpose, usage conditions, return fields, example, and parameter description. Every sentence adds value with no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given a single parameter and presence of an output schema, the description covers input/output adequately and provides usage context. However, it lacks details on error handling or not-found cases, leaving minor gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by detailing the 'query' parameter: it accepts company name, CIF, EU VAT, or LEI, with examples. This is essential for correct invocation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Verify' and resource 'business identity' across 34 countries. It distinguishes itself from siblings like borme_lookup (BORME acts) and verify_vat (VAT) by focusing on identity verification with multiple identifiers.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit 'Use when' with concrete user queries ('is this company legit?', 'check CIF B80988678'), giving clear context. While it doesn't explicitly state when not to use, the examples and sibling names imply alternatives.

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

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