Temsor API — Turkey & EU business data

Phone Number Validation

phone_validate

Validates and normalises a phone number to E.164, classifies the line type, and resolves the province for Turkish landlines.

For signup and checkout flows that need to store one canonical form and reject typos early. Turkish numbers are handled in depth: landline area codes resolve to a province, mobile and special ranges (toll-free 0800, fixed-rate 0850, premium 0900) are classified, and every accepted input comes back in both E.164 and national notation.

One thing this endpoint deliberately does not claim: the current mobile operator. Turkey has had number portability since 2008, so a 0532 number may well be on another network today. Competing APIs report the prefix owner as "the operator" and customers pick SMS routes on that basis. We return it as originallyAllocatedTo with the caveat attached, because a confident wrong answer costs more than an honest gap.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`phone`	Yes	Phone number in any common format.
`defaultCountry`	No	ISO 3166-1 alpha-2 country to assume when the number has no international prefix. Defaults to TR.

TDQS

A4.7/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavioral traits: normalizes to E.164, classifies line types (including special ranges), resolves province for Turkish landlines, and returns both E.164 and national notation. Also honestly states the limitation on operator detection with portability caveat.

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?

Well-structured: opening summary, usage context, detailed behavior, and honest limitation. Each paragraph is purposeful, though the last paragraph could be slightly tighter without losing clarity.

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 no output schema, the description covers key aspects: input format, two output notations, line type classification, province resolution, and operator caveat. It is fairly complete for a complex validation tool, though explicit output structure details would enhance completeness.

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

Parameters4/5

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

Schema coverage is 100% (both parameters described), so baseline 3. The description adds significant semantic value by explaining that defaultCountry defaults to TR, and how Turkish numbers are handled in depth (area codes, special ranges). This goes beyond the schema's minimal descriptions.

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 validates and normalizes phone numbers to E.164, classifies line type, and resolves province for Turkish landlines. It distinguishes from sibling tools by focusing on phone validation with Turkish specifics.

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

Usage Guidelines5/5

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

Explicitly recommends use in signup and checkout flows for canonical storage and early error rejection. Also clarifies what the tool does not do (report current mobile operator) and provides caveats about number portability, helping the agent avoid misuse.

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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