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Ban Fr

by io.github.pipeworx-io
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Geocode

geocode
Read-onlyIdempotent

Forward-geocode a French address, street, locality or commune. Returns scored GeoJSON matches (score 0-1) with label, coordinates, postcode, citycode (INSEE) and context. France only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesFree-text query, e.g. "8 bd du port" or "rue de la paix paris".
latNoLatitude to bias/prioritise results geographically (pair with lon).
lonNoLongitude to bias/prioritise results geographically (pair with lat).
typeNoRestrict to a feature type.
limitNoMax results (default 5, server caps at 20).
citycodeNoFilter by INSEE commune code, e.g. "95127".
postcodeNoFilter by 5-digit postal code, e.g. "95000".
autocompleteNoTreat q as a partial/autocomplete prefix (default false).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "q": "8 bd du port marseille"
+  },
+  {
+    "autocomplete": true,
+    "limit": 10,
+    "postcode": "75002",
+    "q": "rue de la paix"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds valuable context beyond these: it specifies the output format (scored GeoJSON with score 0-1 and specific fields) and the geographic restriction (France only). This enriches the behavioral model without contradicting the annotations.

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 two sentences, front-loaded with the action verb and resource, and every clause adds value. It mentions the output format and scope in a compact manner with zero filler.

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?

For a read-only, non-destructive geocoding tool with a rich input schema, the description covers the main purpose, geographic scope, and output shape (scored GeoJSON, fields included). Since there is no output schema, describing the return format is essential and handled well. It could mention errors or rate limits, but those are less critical for this simple read operation.

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 input schema has 100% parameter description coverage, with each parameter having a detailed description. The tool description adds no additional parameter-level semantics beyond what the schema provides. Baseline 3 is appropriate because the schema does the heavy lifting.

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 function: 'Forward-geocode a French address, street, locality or commune.' This uses a specific verb with a specific resource type and geospatial scope. It distinguishes itself from the sibling 'reverse_geocode' by explicitly using the term 'Forward-geocode' and adding 'France only'.

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 when to use the tool (for forward geocoding) and provides clear context (France only, returns scored GeoJSON matches). It does not explicitly mention alternatives or when-not-to-use, but the contrast with 'reverse_geocode' is strongly implied. The parameter descriptions also offer usage hints (e.g., bias with lat/lon), which adds guidance beyond the main description.

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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