Skip to main content
Glama

IRIS neighbourhood of an address

iris_lookup

Resolve any French address to its official IRIS statistical area, returning the 9-character IRIS code, name, type, and commune. Use this code to link to INSEE population and income data.

Instructions

Identify the IRIS — INSEE's infra-communal statistical unit, about 2 000 inhabitants — containing an address: 9-character IRIS code, name, type and commune. Join that code to INSEE's IRIS tables for population and Filosofi income. Boundaries from IGN ADMINEXPRESS; this layer carries identity only, no socio-demographic figures.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
addressYesAddress in France

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.5.0

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations provided, the description carries the full disclosure burden. It clearly explains what the tool returns, notes the data source (IGN ADMINEXPRESS), and explicitly states the layer has no socio-demographic figures, which is a meaningful behavioral limitation beyond the basic lookup.

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?

Three sentences, each earning its place: the core purpose and outputs, the downstream join possibility, and the data source/limitation. It is front-loaded with the most important action and avoids redundant or filler text.

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 single-parameter lookup with no output schema and no annotations, the description is reasonably complete: it names the output fields, the data source, and the layer's limitation. It does not discuss edge cases like invalid addresses or ambiguous matches, but that is a minor gap for this simple 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 input schema fully describes the single parameter 'address' as 'Address in France,' so schema coverage is 100%. The description adds little beyond that, mentioning the address is used to identify the containing IRIS but not providing formatting or disambiguation guidance; baseline 3 is appropriate.

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 opens with 'Identify the IRIS', a specific verb and resource, and enumerates the exact outputs: 9-character IRIS code, name, type, and commune. It also distinguishes the tool from generic geocoding or commune lookups by specifying INSEE's infra-communal statistical unit.

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?

It provides clear context for use: obtaining the IRIS code for an address and joining it to INSEE demographic/income tables. It also states the layer 'carries identity only, no socio-demographic figures,' which prevents misuse, though it does not explicitly name sibling alternatives or state when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.