Temsor API — Turkey & EU business data

LEI Lookup

lei_lookup

Looks up a Legal Entity Identifier in the GLEIF register: legal name, registration status, country, and BIC.

Checksum-valid is not the same as currently registered — a LAPSED or MERGED LEI still passes ISO 7064. This endpoint checks the digits locally first (a typo never becomes an upstream call) then GET the GLEIF lei-records API. Results are cached for 24 hours. GLEIF allows 60 requests per minute; above that, or on timeout/5xx, we return unknown rather than invent a registration. HTTP 404 means the service answered and the LEI is not in the index (invalid). This is not a replacement for GLEIF’s own API for bulk. status is registration.status (ISSUED/LAPSED/MERGED/…), not entity.status (ACTIVE).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`lei`	Yes	Legal Entity Identifier, 20 characters.

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations, the description fully covers behavioral aspects: it explains the local checksum pre-check, caching (24 hours), rate limits (60/min), error handling (returns 'unknown' on timeout/rate limit, 404 meaning not found), and clarifies that 'status' refers to registration status, not entity status. This is exemplary transparency.

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

Conciseness4/5

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

The description is dense and informative, covering multiple aspects (validation, caching, rate limits, errors, status clarification) without redundancy. It is slightly long but each sentence adds value, so it remains concise and well-structured.

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 the tool's simplicity (one parameter, no output schema), the description covers expected return data, potential errors, and operational constraints. It does not detail the exact JSON output structure, but that is acceptable without an output schema. The description is comprehensive enough for an agent to use the tool correctly.

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

Parameters3/5

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

The schema provides a description for the only parameter 'lei' ('Legal Entity Identifier, 20 characters'), giving full coverage. The tool description adds context about checksum validation but does not significantly enhance the parameter meaning beyond the schema, so baseline 3 applies.

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 tool's purpose: 'Looks up a Legal Entity Identifier in the GLEIF register' and specifies the returned data (legal name, registration status, country, BIC). It distinguishes itself from sibling validators like 'lei_validate' by focusing on lookup rather than validation.

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?

The description implies usage for individual LEI lookups by mentioning checksum pre-check and a single GET request. It explicitly states 'This is not a replacement for GLEIF’s own API for bulk,' providing guidance against bulk use. However, it does not directly compare with sibling tools like 'lei_validate'.

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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TDQS

A4/5.0

Disambiguation4/5

Most tools have clearly distinct purposes (e.g., bic_validate vs vin_validate), but there is notable overlap: tr_validate bundles Turkish validations that are also covered individually by iban_validate and tin_validate. Also, lei_validate and lei_lookup are closely related but distinct enough. Overall, agents can usually pick the right tool, but a few pairs could confuse.

Naming Consistency4/5

Tool names are consistently snake_case with predominately verb_noun patterns (e.g., validate, lookup, screen, parse, build). Some nouns like mcp_index, model_archive, and series_history break the verb pattern but are still predictable. Minor deviations from the noun_verb form (tr_invoice_build, shipping_identify) don't cause confusion. Very readable and consistent overall.

Tool Count3/5

With 27 tools, the set is heavy, exceeding the typical 3–15 well-scoped range. However, the server covers a broad domain: international standards validation, Turkey-specific business data (fuel, labor, invoices, addresses), and even MCP/LLM model archives. The count is justifiable given the scope, but it stretches coherence and may overwhelm agents.

Completeness4/5

The tool surface is remarkably comprehensive for the stated Turkey & EU business data purpose: validators for most ID types, VAT, IBAN, phone, VIN, sanctions; plus Turkey-specific operations like invoice build/parse, labor calculations, fuel prices, business days, and address parsing. Minor gaps exist (e.g., no general exchange-rate conversion, no credit-note-specific builder), but agents can accomplish core workflows without dead ends.

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