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Analyze Market Activity

analyze_market_activity
Read-only

Monthly transaction volume time series — use when you need to show how activity has changed over time, not when you need a single comparable number.

WHEN TO USE THIS vs score_market_health:

  • Use analyze_market_activity when the question is "show me how volume has evolved" or "is there seasonality?"

  • Use score_market_health when the question is "is this market good or bad?" or "compare two markets"

REQUIRED: At least one location filter:

  • code_postal, commune (e.g., "PARIS 11"), or code_departement

Optional:

  • type_local: Property type filter

  • months: Analysis period (default: 24, max: 60)

Returns:

  • Monthly transaction volumes with YoY changes

  • Trend direction: "increasing", "decreasing", or "stable"

  • Activity level: "hot", "normal", or "slow"

  • Seasonality: peak and low months

  • Market signal with description

Example: Lyon market activity: {commune: "LYON", type_local: "Appartement", months: 24}

Note: DVF data covers 2014–2025 (annual cadence). Use months ≥ 12 for reliable results.

Cost: 10 credits per call

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "analyze_market_activity"
  }
}

Input Schema

NameRequiredDescriptionDefault
monthsNoNumber of months to analyze (default: 24)
communeNo
type_localNo
code_postalNo
code_departementNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, and openWorldHint=false, so safety is covered. The description adds behavior the annotations cannot: a cost of 10 credits per call, the DVF data coverage window (2014–2025, annual cadence), and a reliability caveat recommending months >= 12.

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?

Structured with labeled sections (when-to-use, required, optional, returns, example, note, cost) and front-loaded with the one-line purpose. Every line carries information an agent needs; nothing is redundant with the schema or annotations.

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 enumerates the return payload (monthly volumes with YoY changes, trend direction, activity level, seasonality, market signal). Combined with cost, data coverage, and the required-filter rule, an agent has everything needed to call it correctly 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 description coverage is only 20%, so the description carries most of the burden, and it does: it states that at least one of code_postal, commune, or code_departement is required (the schema declares zero required parameters), gives a commune format example ('PARIS 11'), and restates the months default and maximum. It does not specify the expected format for code_postal or code_departement, leaving a small gap.

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 opening line names a specific resource and output shape: 'Monthly transaction volume time series.' It immediately frames what the tool produces and contrasts it with a single-number alternative, so an agent can distinguish it from score_market_health 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 gives an explicit WHEN TO USE THIS vs section with the literal question phrasings that select each tool ('show me how volume has evolved' / 'is this market good or bad?'). It also states the hard precondition of at least one location filter, so the agent knows both when to call it and what input is mandatory.

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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