gapup-mcp

manufacturing_waste_heatmap

Read-onlyIdempotent

Generates manufacturing waste heatmaps for COOs using EPA TRI and FAOSTAT data. Input manufacturing site identifiers or geographic regions to analyze waste streams, emissions, and resource inefficiencies. Outputs include waste intensity maps, circular economy opportunity rankings, and cost-saving potential. Ideal for sustainability strategy and operational efficiency improvements. Pass async:true to avoid timeout.

Input Schema

TableJSON Schema

Name	Required	Description
`year`	Yes	Analysis year (2010-2023)
`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`	No	Geographic region (country code or sub-national region) for aggregated analysis
`site_ids`	No	List of manufacturing site identifiers (EPA TRI IDs or FAO facility codes)
`waste_types`	No	Specific waste types to analyze (e.g., ['metals', 'chemicals', 'energy'])

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`heatmap_data`	No
`opportunities`	No
`benchmark_data`	No

TDQS

A4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, openWorldHint. The description adds that passing async:true avoids timeout, and mentions data sources (EPA TRI, FAOSTAT). It does not contradict annotations, and adds useful behavioral context beyond the structured fields.

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 four sentences, each conveying essential information: purpose and data sources, input types, outputs, and async usage. No unnecessary words, front-loaded with the main action.

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 tool's moderate complexity (5 parameters, output schema exists), the description covers purpose, inputs, outputs, and async guidance. It does not detail return values (output schema handles that) and could mention optionality of waste_types, but overall adequate.

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 the description adds little beyond what the schema provides. It mentions 'manufacturing site identifiers or geographic regions' which maps to site_ids and region, but the schema already explains these clearly. 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 generates waste heatmaps using EPA TRI and FAOSTAT data for COOs, and lists specific outputs (waste intensity maps, circular economy opportunity rankings, cost-saving potential). This is specific and distinguishes it from siblings like manufacturing_esg_compliance_mapper.

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 says 'Ideal for sustainability strategy and operational efficiency improvements', which implies usage context but does not explicitly state when not to use or provide alternatives. With many sibling tools, clearer guidance would help.

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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