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Tanod MCP server

utilpeek: geocode a city name or reverse-geocode coordinates

geocode
Read-onlyIdempotent

Resolve city names to latitude/longitude or coordinates to a city via Tanod MCP server using offline GeoNames cities15000 data.

Instructions

utilpeek: offline geocoding over the GeoNames cities15000 table. For places of a kind nearby use find_places_nearby. Input: either place alone or lat and lon together. place matches cities of 15,000+ people offline ("City" or "City, CC" with an ISO country code or a US state code); no match is a 404 place_not_found (not charged). Cities only (no street addresses). Typically under 0.1 s (first call up to 1 s). Price: USD 0.001. Free: 10 utilpeek calls per IP per UTC day. Data: GeoNames (geonames.org) cities15000, CC BY 4.0; credit it when you show or republish it (the attribution field carries the text). Docs: https://tanod.dev/learn/reverse-geocoding-api.html

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latNoReverse: latitude (-90..90); give lat and lon together, or place.
lonNoReverse: longitude (-180..180); give lat and lon together, or place.
placeNoForward: city name, optionally "City, CC" (ISO country or US state code); give place alone, or lat and lon.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeNo
errorNoOnly on an error result: an object {code, message}, or the reason string of an x402 PaymentRequired object.
queryNo
totalNo
filterNo
resultsNo
attributionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already declare read-only/idempotent/open-world, and the description adds genuinely new behavior: a 404 place_not_found that is not charged, typical latency under 0.1 s with a 1 s first call, price USD 0.001, a 10-call free tier per IP per UTC day, and the attribution obligation carried in the `attribution` field.

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?

Front-loaded with purpose, then the alternative tool, then input rules, then operational facts. Every sentence carries information, though the pricing, license, and docs-link tail makes it denser than strictly necessary for a 3-parameter call.

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?

An output schema exists so return values need no explanation, and the annotations cover the safety profile. What remains — input modes, matching limits, error behavior, cost model, and data provenance — is fully addressed.

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%, so baseline is 3, but the description adds real matching semantics not in the schema: `place` accepts "City" or "City, CC" with an ISO country or US state code, and it only matches cities, never street addresses. The either/or exclusivity rule is also clarified.

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 names a specific verb (geocode) and resource (city names / coordinates) and immediately scopes it to the offline GeoNames cities15000 table. It explicitly distinguishes itself from the sibling find_places_nearby, so an agent can route without opening either schema.

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?

It states the exact input modes (either `place` alone or `lat` and `lon` together), names the alternative tool for nearby-place queries, and gives the matching scope (cities of 15,000+ people, no street addresses). When-to-use and when-not-to-use are both covered.

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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