gapup-mcp

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

Name	Required	Description
`mode`	Yes	Analysis mode.
`async`	No	If 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.
`query`	Yes	Company name, ticker, ISIN or LEI (e.g. "Microsoft", "Sanofi", "Volkswagen").
`pillar`	No	ESG pillar filter (optional, default: all).
`framework`	No	ESG framework filter (optional, default: all).

Output Schema

TableJSON Schema

Name	Required	Description	Default
`mode`	Yes
`status`	Yes
`signals`	Yes
`sources`	Yes
`emissions`	No
`company_score`	No
`controversies`	No
`quality_score`	Yes
`esrs_readiness`	No
`sfdr_classification`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint=true and destructiveHint=false, so the description adds value by detailing data sources (CDP, SBTi, etc.), signal severity levels (P0/P1/P2), cache duration, and per-mode behavior. No contradiction with annotations.

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 bulleted modes and a separate signals section. However, it is verbose (200+ words) and could be trimmed by removing redundant phrasing. The information density is good but not maximally concise.

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 (5 modes, multiple data sources, signal system, async support, cache), the description covers all key aspects: what each mode returns, data sources, signal levels, and performance characteristics. No gaps are apparent.

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?

Schema coverage is 100%, so parameters are already described. The description adds context for the 'async' parameter and lists modes in prose, but this largely repeats the schema enum. It does not significantly augment parameter understanding beyond what the schema provides.

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 'Multi-mode ESG intelligence' and enumerates five specific modes with distinct outputs (company_score, controversy_check, etc.). It differentiates itself from sibling tools by covering multiple ESG analysis types in one tool.

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 target audience is specified (ESG analysts, sustainability officers, impact investing fund managers) and each mode's output is described, implying when to use each. However, explicit guidance on when not to use this tool versus alternatives like supplier_esg_audit or carbon_footprint_calculator is missing.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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