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mcp-knowledge

by io.github.getgapup

logistics_esg_incident_tracker

Read-onlyIdempotent

Tracks real-time ESG incidents in logistics networks for COOs, including supply chain disruptions, regulatory violations, and sustainability risks. Inputs: geographic region, incident type (e.g., emissions, labor, deforestation), and time range. Outputs: structured incident data with severity, location, and source verification. Uses CDP open data and UNCTAD STAT for comprehensive coverage. Keywords: ESG, logistics, supply chain, sustainability, compliance, risk management.

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.
regionYesGeographic region filter (e.g., 'Europe', 'Asia', 'Global')
endDateNoEnd date for incident search (ISO 8601)
severityNoMinimum severity level to include
startDateNoStart date for incident search (ISO 8601)
incidentTypeYesType of ESG incident to track

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
summaryNo
warningsNo
incidentsNo

TDQS

B3.4/5.0
Behavior3/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent. Description adds data sources and real-time aspect. However, it fails to mention the async parameter behavior (schema includes async boolean) and any rate limits – important for behavioral understanding.

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?

Description is front-loaded with purpose and concise. The trailing keyword list is somewhat redundant; could be omitted.

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?

Covers main aspects (inputs, outputs, data sources) but lacks explanation of async execution and differentiation from similar tools. Output schema exists, so output description is sufficient.

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%; description lists main parameters but misses 'severity' and 'async'. Does not add significant meaning beyond schema descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Tracks real-time ESG incidents in logistics networks for COOs' and lists specific resources and inputs/outputs. However, it does not differentiate from many sibling ESG tools (e.g., supplier_esg_audit, esg_audit_multi) that may overlap.

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?

Purpose implies use for COOs monitoring ESG incidents, but no explicit guidance on when to use vs alternatives (siblings include several ESG tools) or when not to use.

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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