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mcp-knowledge

by io.github.getgapup

carbon_footprint_calculator

Read-onlyIdempotent

Calculate a company's greenhouse-gas footprint under the GHG Protocol (Scope 1 + 2 + 3, in tCO2eq, tier-2 accuracy ±20%). Returns the emissions breakdown, hotspot identification, 5-8 reduction levers each with capex and payback, an SBTi-aligned reduction trajectory over 5-25 years, the 15 Scope-3 categories in detail, and CSRD/ESRS reporting readiness. When to use this tool: the user needs a carbon assessment for CSRD compliance pre-audit, green-finance access, or supplier ESG scorecards. Inputs: the company profile and its activity data. Delivered by Émilie, the AI Sustainability lead of the Gapup portfolio.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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.
focusNo
companyYes
perimeterYes
scope1SourcesNo
scope2SourcesYes
reductionTargetsNo
scope3ActivitiesNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
kpisNo3-5 headline ESG KPI bubbles
hotspotsYesTop emission sources ranked by contribution
breakdownYesEmissions breakdown by scope
csrdReadinessYesCSRD/ESRS reporting readiness assessment
sbtiTrajectoryNoSBTi-aligned annual reduction trajectory
reductionLeversYes5-8 actionable reduction levers with financial analysis
executiveSummaryYesBoard-ready GHG assessment prose
scope3CategoriesNoGHG Protocol 15 Scope-3 categories detail
totalEmissionsTco2eqYesTotal GHG footprint in tCO2eq (Scope 1+2+3 combined, ±20% tier-2 accuracy)

TDQS

A4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, idempotentHint, destructiveHint=false. The description adds behavioral details: output includes emissions breakdown, hotspot identification, reduction levers, SBTi trajectory, Scope-3 categories, and CSRD readiness. No contradictions 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.

Conciseness5/5

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

The description is concise (a few sentences) with front-loaded core purpose. Every sentence adds value: purpose, scope, outputs, use cases, inputs, and persona. No fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given complexity (3 scopes, many outputs, many input parameters) and existence of output schema, the description covers outputs well but inputs vaguely. With low schema coverage, it's incomplete regarding parameter details.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is only 13%, meaning most parameters lack descriptions. The description only vaguely mentions 'company profile and its activity data' without detailing individual parameters. With such low coverage, the description should compensate but fails to do so.

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 verb 'calculate' and the resource 'greenhouse-gas footprint' with specifics: GHG Protocol, Scopes 1-3, unit tCO2eq, tier-2 accuracy ±20%. It distinguishes from siblings like carbon_roadmap by focusing on comprehensive assessment.

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?

Provides explicit when-to-use scenarios: CSRD compliance pre-audit, green-finance access, supplier ESG scorecards. Does not explicitly state when not to use or mention alternative tools, but the scenarios are clear enough.

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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