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gapup-mcp

by io.github.getgapup

esg_audit_multi

Read-only

Multi-mode ESG intelligence for ESG analysts, sustainability officers and impact investing fund managers. Aggregates live data from CDP, SBTi, Wikipedia, Yahoo Finance and web search across five modes: • company_score — ESG score 0-100 with E/S/G breakdown + heuristic rating (AAA-CCC), from CDP grade + SBTi + sector profile • controversy_check — controversies detected via web search, classified P0/P1/P2 by type (greenwashing, emissions fraud, labour, governance) • emissions — GHG Scope 1/2/3 estimates, SBTi validation flag, net-zero target year, carbon intensity per M€ revenue • esrs_readiness — CSRD gap across 12 standards (E1-E5, S1-S4, G1-G3): readiness % + gap list + CSRD deadline + effort man-days • sfdr_classification — suggested SFDR Article 6/8/9 with rationale and sustainability indicators met

Signals: P0=critical (controversy/score<40), P1=significant (score<55/SBTi missing/ESRS<50%), P2=watch. Cache 24h.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYesAnalysis mode.
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.
queryYesCompany name, ticker, ISIN or LEI (e.g. "Microsoft", "Sanofi", "Volkswagen").
pillarNoESG pillar filter (optional, default: all).
frameworkNoESG framework filter (optional, default: all).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
modeYes
statusYes
signalsYes
sourcesYes
emissionsNo
company_scoreNo
controversiesNo
quality_scoreYes
esrs_readinessNo
sfdr_classificationNo

TDQS

A4.4/5.0
Behavior4/5

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

The description adds valuable behavior beyond annotations: it lists data sources (CDP, SBTi, Wikipedia, Yahoo Finance, web search), mentions caching (24h), and explains signal priority levels (P0/P1/P2). This supplements the read-only and open-world hints without contradiction.

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?

The description is structured with bullet points and clear sections, front-loading purpose and audience. While lengthy, it earns its length for a complex five-mode tool; no redundant content.

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?

For a tool with five modes and complex outputs, the description thoroughly covers each mode's deliverables, signal logic, data sources, and caching. The presence of an output schema reduces the need to document return formats, and the description is complete for selection and invocation.

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%, so baseline is 3. The description enriches the 'mode' parameter by detailing outputs for each mode, and provides examples of valid query inputs, adding meaning beyond the schema's simple 'Analysis mode' description.

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 multi-mode ESG intelligence tool with five distinct modes, each with specific outputs and data sources. It distinguishes itself from siblings by its comprehensive scope and audience focus.

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 clear context by listing intended users and enumerating modes, making it obvious when to use the tool. It does not explicitly name alternatives or exclusion criteria, but the context is unambiguous.

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.

Resources