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normi

by io.github.NormiHQ
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Resolve Location

resolve_location
Read-only

Resolve free-form French administrative locations to the canonical Normi filters.

Use this before another Normi tool when the location is informal, accented, abbreviated, or ambiguous. It accepts postal codes, communes and arrondissements such as "Paris 11e", "75011", "Saint-Étienne" or "st etienne".

It returns ranked canonical commune, postal-code, department and INSEE filters. "Paris" intentionally returns all 20 arrondissements rather than silently choosing one.

Cost: 1 credit per call

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "resolve_location"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYesFree-form administrative location, e.g. 'Paris 11e', '75011', 'Saint-Étienne', or 'st etienne'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Beyond the readOnly/openWorld/destructive annotations, it discloses two non-obvious behaviors: results are ranked, and a bare 'Paris' deliberately returns all 20 arrondissements rather than picking one. It also discloses a billing cost of 1 credit per call, which no annotation conveys.

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 usage, then return behavior, then cost — a sensible order with no filler. The one inefficiency is the example list, which duplicates the schema's own examples verbatim, though it does help sales-pitch the accepted formats.

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?

No output schema exists, and the description compensates by describing the returned filter types and their ranking. Combined with the invocation preconditions and cost disclosure, an agent has everything needed for a one-parameter lookup tool.

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 single parameter is 100% documented in the schema, including the same example strings used in the description, so the description adds little new meaning. Baseline 3 applies when schema coverage is complete; the description does at least confirm the accepted input families (postal codes, communes, arrondissements).

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?

States a specific verb and resource ('Resolve free-form French administrative locations to the canonical Normi filters') and makes the output concrete (commune, postal-code, department, INSEE filters). An agent can immediately distinguish this from analytical siblings like compare_locations or search_property_transactions.

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?

Gives an explicit sequencing rule ('Use this before another Normi tool') plus the triggering conditions (informal, accented, abbreviated, ambiguous locations). The only gap is that it doesn't name which tool to call instead when the location is already canonical, but the conditional framing is unusually strong.

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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