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gapup-mcp

by io.github.getgapup

real_estate_intel

Read-onlyIdempotent

Real estate intelligence aggregator with a best-in-class French dataset (DVF — Demandes de Valeurs Foncières — 100% of FR transactions since 2019, public, keyless) plus UK Land Registry Price Paid (all UK transactions 1995+). Four modes: (1) property — full transaction history for a specific address; (2) comparables — median/std price/m² within a radius (default 500m); (3) market — annual price series, YoY change, volume, trend by commune; (4) valuation — two-method estimate (comparables median + hedonic regression if n≥30) with confidence scoring (high/medium/low). All sources are free and require no API key. ICP: PropTech agents, REITs, fund managers, family offices, insurance. SLA: ≤25s p95 (sources fetched in parallel, 8s budget each). Cache: 24h TTL (DVF data is stable). Quality score: 30 pts DVF retrieved, 20 pts geocoding, 20 pts UK LR retrieved, 15 pts if comparables count ≥10, 15 pts if method quality achieved. Status: failed/<60/≥60 → failed/partial/final. No env vars required.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesproperty: transactions at an address | comparables: sample around a point | market: commune/neighbourhood market stats | valuation: price estimate for a given surface
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
date_toNoISO date YYYY-MM-DD — latest transaction date
locationYesLocation descriptor. One of: {address, city?, country?} | {lat, lon, radius_m?} | {insee_code} for FR communes.
date_fromNoISO date YYYY-MM-DD — earliest transaction date
max_resultsNoMaximum number of results to return (5–50, default 20)
surface_maxNoMaximum surface in m² (±20% tolerance applied for comparables)
surface_minNoMinimum surface in m² (±20% tolerance applied for comparables)
property_typeNoFilter by property type (default: all)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
marketNomode=market — commune-level market stats
statusYes
sourcesYes
propertyNomode=property — transactions at the location
valuationNomode=valuation — price estimate
comparablesNomode=comparables — aggregated comp stats
quality_scoreYes
location_resolvedYes

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds a wealth of operational context: SLA (≤25s p95), cache TTL (24h), quality score computation, and status semantics (failed/partial/final). It also notes that no API key or env vars are required, making execution expectations clear.

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 dense but efficient, front-loading the core purpose and then itemizing modes and operational details. Each sentence contributes unique information (data sources, SLA, caching, quality scoring), so there is no waste.

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?

Given the tool's complexity (9 params, 4 modes, nested objects) and the presence of a full output schema, the description provides comprehensive coverage: data jurisdictions, mode behavior, caching, SLA, and quality metrics. The only minor gap is mode-to-parameter mapping, but the schema compensates.

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 schema already documents every parameter with 100% coverage, so the baseline is 3. The description adds some semantic context (e.g., radius default, mode meanings) but does not substantially extend parameter understanding beyond the schema.

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 identifies the tool as a real estate intelligence aggregator with four distinct modes (property, comparables, market, valuation), each with a concrete definition. It distinguishes itself from sibling tools by naming its specific data sources (DVF + UK Land Registry) and output types.

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 gives explicit usage context: aimed at PropTech agents, REITs, fund managers, etc., and explains each of the four modes so the agent can select the appropriate one. It does not name alternatives or exclusion rules, which prevents a 5.

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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TDQS

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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