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mcp-knowledge

by io.github.getgapup

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
NameRequiredDescriptionDefault
yearNoReporting year (default: current year)
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.
vendorIdNoOptional identifier (e.g., LEI, DUNS)
vendorNameYesLegal name of the vendor to check

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
vendorIdNo
warningsYes
griAlignedNo
vendorNameYes
violationsNo
blacklistedYes
esgRiskScoreNo
cdpDisclosureScoreNo

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, idempotentHint=true, and openWorldHint=true, indicating a safe, idempotent query. The description adds detail about the response data (CDP score, GRI alignment, regulatory flags) and mentions the async parameter behavior, providing useful context beyond 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 concise (4 sentences) and front-loaded with the tool's core function. The keyword list is slightly redundant but does not detract significantly. Every sentence adds value.

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 existence of an output schema, the description sufficiently explains what the tool returns. It covers the main use case and data points, making it complete for a query tool.

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 well-documented. The description reinforces that vendorName is the legal name and vendorId is an identifier (e.g., LEI, DUNS), but adds minimal semantics beyond the schema. Baseline 3 is appropriate.

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: 'check if a vendor is blacklisted for ESG non-compliance using CDP and GRI data.' It specifies the action, resource, and data sources. The sibling list includes other vendor risk tools, but this tool's focus on ESG blacklist is distinct.

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 usage context: 'Ideal for vendor due diligence, risk assessment, and sustainability reporting.' It implies when to use the tool but does not explicitly compare with alternatives or state when not to use it.

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.

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