Skip to main content
Glama

mcp-knowledge

by io.github.getgapup

manufacturing_esg_compliance_mapper

Read-onlyIdempotent

As a COO, quickly identify ESG compliance gaps across manufacturing facilities using EPA TRI emissions data and GRI sustainability standards. Input facility identifiers or geographic regions to receive a prioritized remediation roadmap with risk scores, regulatory violations, and suggested corrective actions. Ideal for sustainability reporting, regulatory risk assessment, and operational improvement planning. Keywords: ESG compliance, manufacturing facilities, EPA TRI, GRI standards, sustainability reporting, regulatory risk.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoReporting year (default: current year - 1)
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.
regionNoGeographic region (state, county, or ZIP code) for facility search
includeGriNoInclude GRI standards analysis (default: true)
facilityIdsYesList of EPA facility identifiers (e.g., TRIFID)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesYes
summaryNo
warningsYes
facilitiesYes

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already provide readOnlyHint, openWorldHint, and idempotentHint, so the agent knows the tool is safe and idempotent. The description adds 'quickly identify' and output format but does not disclose additional behavioral traits like rate limits or authentication needs, adding minimal 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.

Conciseness4/5

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

The description is three sentences with keywords, efficiently front-loaded with the main action. It could be slightly more concise by removing 'As a COO' and the keyword list, but overall it is well-structured and not verbose.

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 has 5 parameters and an output schema, the description adequately covers the main purpose, data sources, and output types. It does not mention the year or async parameters, but these are detailed in the schema. The description is sufficient for an agent to understand context and appropriate use.

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% with descriptions for all 5 parameters. The description only mentions facilityIds and region in text, but does not add semantics beyond what the schema provides. Baseline 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 identifies ESG compliance gaps across manufacturing facilities using EPA TRI and GRI standards. It specifies inputs (facility identifiers or regions) and outputs (roadmap with risk scores, violations, actions), making the purpose distinct from siblings like esg_audit_multi or sustainability_report.

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 lists ideal use cases (sustainability reporting, regulatory risk assessment, operational improvement) but does not specify when to avoid the tool or mention alternative tools. No exclusions or when-not guidance is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All mcp-knowledge toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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.

Resources