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mcp-knowledge

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

A4.2/5.0
Behavior4/5

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

Annotations mark the tool as read-only and non-destructive. The description adds context: it aggregates live data from CDP, SBTi, etc., caches results for 24h, and describes signal levels (P0/P1/P2). This goes beyond annotations without contradicting them.

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 well-structured with bullet points and front-loaded purpose. However, it is somewhat verbose with redundant details (e.g., signals listed twice). Each section earns its place, but trimming could improve conciseness.

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?

The description covers modes, data sources, signals, and cache, but omits explanation of the 'async' parameter (though schema covers it). No mention of pagination or rate limits. Given complexity with 5 parameters and an output schema, some gaps remain.

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 100%, so baseline is 3. The description adds value by explaining each mode's output and the meaning of signals, which enhances the understanding of the 'mode' parameter beyond its schema description ('Analysis mode.').

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 the tool's purpose as 'Multi-mode ESG intelligence' with five explicit modes (company_score, controversy_check, emissions, esrs_readiness, sfdr_classification). Each mode is briefly explained with outputs, making it specific and distinct from sibling tools like 'supplier_esg_audit' or 'carbon_footprint_calculator'.

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 implies usage context by listing modes for different ESG needs (scores, controversies, emissions, etc.) but does not explicitly tell when to use this tool versus alternatives. No when-not or alternative recommendations are provided.

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.

Resources