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Analyze Building Age Price Impact (BDNB)

analyze_building_age_price_impact
Read-only

Get median prix/m² grouped by construction decade for a location.

Shows how building age affects price — the premium or discount of each era vs. the area median. Useful for advising whether older buildings trade at a discount and how much.

Construction decade buckets: avant_1919, 1919_1945, 1946_1970, 1971_1990, 1991_2005, 2006_plus

REQUIRED: at least one location — code_postal, commune, or code_departement OPTIONAL: type_local (Maison|Appartement)

Example output: { tranche: "1946_1970", nb: 312, prix_m2_median: 3800, vs_zone_pct: -12 } → buildings from 1946-1970 sell 12% below the area median

Cost: 10 credits

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
communeNoCommune name (e.g., 'LYON', 'PARIS 11')
type_localNoProperty type filter: Maison or Appartement (default: all)
code_postalNoPostal code (e.g., '69001')
code_departementNoDepartment code (e.g., '69', '75')

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint, so the safety profile is covered. The description adds genuinely non-derivable context: the 10-credit cost, the at-least-one-location constraint, and the exact decade buckets returned. It stops short of describing pagination or result limits, keeping it out of the top tier.

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 the core operation, then organized into labeled sections (buckets, required/optional, example, cost) that are easy to scan. The example-output line and its interpretation earn their place, though the block is longer than strictly minimal.

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?

With no output schema, the description compensates by supplying a concrete example output row plus interpretation, the full set of decade bucket keys, the location requirement, and the cost. An agent has everything needed to invoke and interpret the result.

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%, which normally anchors a 3, but the description adds meaning the schema lacks: the schema declares no required fields, while the description states 'REQUIRED: at least one location — code_postal, commune, or code_departement', which is a real constraint not encoded anywhere in the schema. That lifts it above baseline.

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 precise verb (Get median prix/m²) and a specific grouping dimension (construction decade) for a location, which no sibling tool covers — nothing else in the sibling list analyzes building age. An agent can select this without opening the schema.

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?

Provides a clear use context ('advising whether older buildings trade at a discount and how much'), which tells the agent when this is relevant. However, it never names an alternative (e.g., analyze_dpe_price_premium or get_zonal_price_distribution) or states 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.

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