gapup-mcp

working_capital_esg_impact_rater

Read-onlyIdempotent

As a CFO, assess how ESG factors (Environmental, Social, Governance) influence working capital efficiency using IMF SDR and BIS data. Inputs include company sector, geographic exposure, and ESG risk scores. Outputs provide a quantitative impact rating on working capital metrics like days sales outstanding (DSO) and inventory turnover, alongside IMF SDR-aligned liquidity risk indicators.

Input Schema

TableJSON Schema

Name	Required	Description
`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.
`region`	Yes	Primary geographic exposure (e.g., 'EU', 'APAC')
`sector`	Yes	Industry sector (e.g., 'manufacturing', 'energy')
`currency`	No	Reporting currency (ISO 4217 code, e.g., 'USD', 'EUR')
`esgRiskScore`	Yes	Aggregate ESG risk score (0-100)
`workingCapitalRatio`	No	Current working capital ratio (current assets / current liabilities)

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes
`sources`	No
`warnings`	No
`impactRating`	No	ESG impact on working capital efficiency (-100 to +100)
`esgFactorBreakdown`	No
`liquidityRiskIndicator`	No	IMF SDR-aligned liquidity risk score (0-1)
`workingCapitalAdjustment`	No	Projected adjustment to working capital ratio (%)

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint, so the safety profile is clear. The description adds context: it uses IMF SDR and BIS data, and produces a quantitative impact rating alongside liquidity risk indicators. No contradictions. This adds moderate value 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.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences long, front-loaded with the core purpose, and contains no extraneous text. Every sentence contributes meaningful information about inputs, outputs, and data sources. Efficient and well-structured.

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?

The tool has an output schema (not shown but indicated), so the description does not need to detail return values. It explains the outputs (impact rating on working capital metrics, liquidity risk indicators) and data sources. It does not mention the async parameter, but that is a common meta-parameter understood from the schema. Overall, adequate for an agent to use the 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%—every parameter has a description in the schema. The tool description only summarizes inputs (sector, geographic exposure, ESG risk scores) without adding new semantic detail beyond what the schema already provides. Baseline score of 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: 'assess how ESG factors influence working capital efficiency' using specific data sources (IMF SDR, BIS). It names the main inputs and outputs, including 'quantitative impact rating on working capital metrics like DSO and inventory turnover'. This distinguishes it from sibling tools like 'working_capital' which likely lacks the ESG focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by a CFO evaluating ESG impact on working capital, but does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'working_capital', 'supplier_esg_audit'). No exclusion criteria or prerequisites are mentioned, which is a gap given the many related sibling tools.

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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