gapup-mcp

vendor_esg_blacklist_monitor

Read-onlyIdempotent

As a COO, quickly check if a vendor is blacklisted for ESG non-compliance using CDP and GRI data. Input the vendor's legal name or identifier to receive their ESG risk score, blacklist status, and compliance violations. Returns structured data including CDP disclosure score, GRI alignment, and any regulatory flags. Ideal for vendor due diligence, risk assessment, and sustainability reporting. Keywords: ESG, vendor risk, compliance, CDP, GRI, sustainability, blacklist.

Input Schema

TableJSON Schema

Name	Required	Description
`year`	No	Reporting year (default: current year)
`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.
`vendorId`	No	Optional identifier (e.g., LEI, DUNS)
`vendorName`	Yes	Legal name of the vendor to check

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	Yes
`vendorId`	No
`warnings`	Yes
`griAligned`	No
`vendorName`	Yes
`violations`	No
`blacklisted`	Yes
`esgRiskScore`	No
`cdpDisclosureScore`	No

TDQS

A4.1/5.0

Behavior4/5

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

Annotations (readOnlyHint, idempotentHint) already declare safe read behavior. Description adds context about returned data (CDP disclosure score, GRI alignment, regulatory flags) and mentions quick response, but does not discuss async behavior or rate limits. 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?

Description is concise with 4 sentences covering purpose, input, output, and use case. The opening 'As a COO' is slightly unnecessary but does not harm clarity. Ends with relevant keywords.

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 the tool's moderate complexity (4 parameters, existing output schema), the description adequately covers what the tool does, what inputs are needed, and what kind of outputs to expect. Missing details about async parameter behavior or exact output structure, but output schema exists to fill that gap.

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 description coverage is 100%, so parameters are already documented. Description loosely refers to input (legal name or identifier) but does not add meaningful constraints, formatting, or relationships 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?

Description uses specific verb 'check if a vendor is blacklisted for ESG non-compliance', clearly identifies the resource (vendor ESG blacklist) and data sources (CDP, GRI). It distinguishes from siblings like vendor_esg_diversity_scanner and vendor_risk_assessor by focusing on blacklist status with specific frameworks.

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?

Explicitly states ideal use cases: vendor due diligence, risk assessment, sustainability reporting. However, it does not explicitly contrast with similar sibling tools or state when not to use it, leaving some ambiguity.

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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