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re_deal_screener

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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

A3.8/5.0
Behavior3/5

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

The description adds context beyond the annotations by mentioning 'structured, audited deliverable' and server-side validation. However, it does not elaborate on potential latency, error conditions, or the meaning of openWorldHint, leaving room for more transparency.

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 longer than necessary and includes marketing jargon ('Gapup agent-payable C-suite expertise') that adds little value. It is front-loaded with purpose, but the extensive example list and reference case, while useful, could be trimmed for conciseness.

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?

Given no output schema, the description clearly states the return elements (cap rate, location score, risk flags, recommendation) and provides a concrete reference case. It also mentions data sources and server-side validation, offering sufficient context for an agent to use the tool effectively, though it omits async behavior details.

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?

With only 11% schema coverage, the description partially compensates by illustrating address, deal_type, asking price, units/keys, sqm, and NOI through examples and a reference case. Yet it leaves async, country_iso2, and investment_thesis unexplained, so it does not fully cover the parameter space.

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 a specific verb ('Screen') and resource ('real estate deal (EU)'), with explicit output components (cap rate vs market, location score, risk flags, deal recommendation). It distinguishes itself from sibling tools like ma_deal_screener by focusing on EU/FR real estate with DVF/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?

Provides multiple example prompts showing exactly when to use the tool (e.g., 'Should I pursue this hotel investment...', 'What is the real estate market valuation...'). However, it does not explicitly state when not to use it or name alternatives, so it falls short of 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.