gapup-mcp

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

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.
`focus`	No
`company`	Yes
`perimeter`	Yes
`scope1Sources`	No
`scope2Sources`	Yes
`reductionTargets`	No
`scope3Activities`	No

Output Schema

TableJSON Schema

Name	Required	Description
`kpis`	No	3-5 headline ESG KPI bubbles
`hotspots`	Yes	Top emission sources ranked by contribution
`breakdown`	Yes	Emissions breakdown by scope
`csrdReadiness`	Yes	CSRD/ESRS reporting readiness assessment
`sbtiTrajectory`	No	SBTi-aligned annual reduction trajectory
`reductionLevers`	Yes	5-8 actionable reduction levers with financial analysis
`executiveSummary`	Yes	Board-ready GHG assessment prose
`scope3Categories`	No	GHG Protocol 15 Scope-3 categories detail
`totalEmissionsTco2eq`	Yes	Total GHG footprint in tCO2eq (Scope 1+2+3 combined, ±20% tier-2 accuracy)

TDQS

B3.4/5.0

Behavior3/5

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

Annotations (readOnlyHint, idempotentHint, destructiveHint) already cover safety and idempotency. The description adds accuracy (±20%) and output details but does not disclose behavioral traits like execution time or side effects beyond the schema.

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 a single coherent paragraph with logical flow: what it does, outputs, when to use, inputs. However, the 'Delivered by Émilie' line is extraneous and adds no value, preventing a perfect score.

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

Completeness2/5

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

For a complex tool with 8 parameters and low schema coverage, the description provides a good overview of outputs and purpose but lacks detail on input parameters and their relationships. The existence of an output schema helps, but gaps remain.

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

Parameters1/5

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

Schema description coverage is only 13%, and the description only vaguely mentions 'company profile and its activity data.' It fails to elaborate on the eight parameters, including nested objects like company, perimeter, and emission sources, leaving the agent with insufficient guidance.

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 calculates a company's greenhouse-gas footprint under the GHG Protocol, specifying scope, accuracy, and outputs. It distinguishes from siblings like 'esg_audit_multi' by focusing on carbon footprint with precise protocol and tiers.

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 explicitly provides use cases: 'CSRD compliance pre-audit, green-finance access, or supplier ESG scorecards.' It offers clear context but does not specify when not to use or mention alternative tools, which slightly reduces the score.

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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