gapup-mcp

procurement_okr_esg_aligner

Read-onlyIdempotent

Aligns procurement OKRs with ESG targets for COOs using GRI standards and EU TED procurement benchmarks. Inputs include procurement objectives and ESG focus areas (e.g., carbon reduction, supplier diversity). Outputs structured alignment scores, gap analysis, and actionable recommendations. Essential for COOs integrating sustainability into procurement strategy. Keywords: procurement, ESG, GRI, EU TED, OKR alignment, sustainability metrics.

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.
`esgFocusAreas`	Yes
`industrySector`	No
`procurementObjectives`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`alignmentScores`	No
`recommendations`	No
`benchmarkComparison`	No
`overallAlignmentScore`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, open-world. Description adds value by mentioning GRI standards and EU TED benchmarks used, and outputs (scores, gap analysis, recommendations). No contradiction.

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?

Four sentences, each adding distinct information: purpose, inputs/outputs, target user, keywords. No redundancy; front-loaded with the core function.

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 output schema exists, description sufficiently covers inputs, outputs, standards, and user. Complexity is moderate and adequately addressed for a niche 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?

Description covers the two required parameters with examples (e.g., carbon reduction for ESG focus areas). Optional parameters (industrySector, async) are not mentioned. Schema coverage is 25%, so description partially compensates but could be more thorough.

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?

Clear verb 'aligns' and resource 'procurement OKRs with ESG targets'. Distinguishes from siblings by specifying COOs, GRI standards, and EU TED benchmarks. No ambiguity.

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 target user (COOs) and context (integrating sustainability into procurement strategy). Does not mention when not to use or list alternatives, but context is clear for a specialist tool.

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.

Resources

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