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FabTally Valid

by io.github.zvmzaretsky

Resolve a country / state / city to ISO codes (paid $0.002)

geo_lookup

PAID $0.002 (x402, USDC on Base). Resolve a place against a bundled open dataset (no per-day API limits). Give {query:"Munich, Bavaria, Germany"} OR {country, state, city} and get normalized names, ISO 3166-1/-2 codes, the country→subdivision→city hierarchy, population and coordinates, and confirmation the place exists. Without payment returns the x402 challenge; pass x_payment to settle.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
cityNoCity name.
queryNoFree-text place, e.g. "Munich, Bavaria, Germany" or "France".
stateNoState/province/subdivision name or code (within the country).
countryNoCountry name or ISO 3166-1 alpha-2/alpha-3 code.
x_paymentNox402 payment payload (base64) for this PAID validation. If supplied it is forwarded as the X-PAYMENT header to settle the call and return the real result instead of a 402 challenge. Omit to get the price challenge first.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

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 critical behaviors: the paid x402 challenge flow, the need to pass x_payment to settle, and the absence of per-day API limits. It also states that the tool confirms existence and returns normalized data, leaving no major behavioral surprises.

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 compact and front-loaded with the critical 'PAID' flag and core purpose. Each sentence earns its place, covering payment, input modes, outputs, and the challenge workflow in a dense but readable format.

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

Completeness5/5

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

Given there is no output schema, the description compensates by listing the return values (normalized names, ISO codes, hierarchy, population, coordinates) and explaining the payment prerequisite and challenge behavior. It also mentions no rate limits, providing a complete operational picture.

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?

The schema covers all 5 parameters with descriptions, giving a baseline of 3. The description adds semantic value by explaining the OR relationship between 'query' and the structured fields, and clarifies that x_payment is forwarded as the X-PAYMENT header to settle the call. This goes beyond the basic parameter 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 uses a specific verb 'Resolve' with a clear resource ('a place against a bundled open dataset') and enumerates the exact outputs (ISO codes, hierarchy, population, coordinates). It also distinguishes itself from sibling validation tools (VIN, email, IBAN, VAT) by its focus on geographic 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 gives clear input modes ('Give {query:...} OR {country, state, city}') and explains the payment workflow, making it obvious when and how to use the tool. It does not explicitly name alternatives or exclusion criteria, but the sibling tool names make it clear this is the only geographic lookup tool.

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

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct input type (VIN, place name, email, IBAN, VAT) with no overlap in purpose. An agent can easily distinguish decode_vin from validate_email based on the resource being processed.

Naming Consistency3/5

Three tools use the validate_* prefix (validate_email, validate_iban, validate_vat), but decode_vin and geo_lookup break the pattern with different verb/noun structures. The names are readable but not uniformly consistent.

Tool Count5/5

Five tools form a well-scoped validation utility set, covering common data types (VIN, email, IBAN, VAT, location) without redundancy or bloat. This is an ideal size for a focused server.

Completeness4/5

The set covers a solid range of common validation/lookup tasks, but typical additions like phone or credit-card validation are absent. These are minor gaps that do not hinder the core purpose.

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