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mcp-knowledge

by io.github.getgapup

re_deal_screener

Read-only

Screener deal immobilier (EU) — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Screen this real estate deal: , , asking € — give me cap rate vs market, location score, risk flags, and deal recommendation. · Should I pursue this hotel investment at for € with keys? Run an EU deal screener with DVF comparables and Géorisques risk data. · What is the real estate market valuation for a at based on recent French DVF transactions? · Run a due diligence deal screen on this property: , €, sqm — flood risk, cap rate, price vs comparables. · Evaluate this commercial real estate deal for an investment committee: at , €, NOI €. Reference case: Hôtel boutique 45 keys · 12 rue de la Paix 75002 Paris · €12.5M · €277k/key · comp DVF €250-380k/key · location 92/100 · score 72 · pursue-with-conditions. Inputs are validated server-side — send the documented case fields.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
addressYes
deal_typeYes
country_iso2YesFR
units_or_keysNo
gross_area_sqmNo
current_noi_eurNo
asking_price_eurYes
investment_thesisNo

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description does not need to restate. It adds context about using external data sources (DVF, Géorisques) and returning an audited deliverable, but no behavioral traits beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph with multiple sentences mixing purpose, examples, and a reference case. It front-loads the purpose but includes redundant example variations. Could be more concise by separating usage from examples.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Without an output schema, the description explains the deliverable components (cap rate vs market, location score, risk flags, deal recommendation). It covers required parameters via examples but misses optional parameters. Given 9 parameters and no output schema, completeness is adequate but not thorough.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 11% (async parameter described). The description mentions address, deal_type, price in examples but does not explain other parameters like units_or_keys, gross_area_sqm, current_noi_eur, investment_thesis. It fails to compensate for low schema coverage.

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 it screens EU real estate deals, returns structured deliverable with specific outputs (cap rate, location score, risk flags, recommendation). It distinguishes from siblings like ma_deal_screener and real_estate_intel by focusing on EU deals with DVF comparables and Géorisques data.

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 provides explicit example queries showing when to use (e.g., 'Screen this real estate deal: ...', 'Should I pursue this hotel investment ...'). It implies EU focus but does not explicitly state when not to use, such as for non-EU deals or M&A contexts.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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