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mcp-knowledge

by io.github.getgapup

supply_chain_fx_exposure_dashboard

Read-onlyIdempotent

Provides real-time foreign exchange exposure dashboard for supply chain monitoring. Designed for COO persona to track currency risk across suppliers and regions. Inputs include supplier IDs, base currency, and target currencies. Outputs structured FX exposure data with risk indicators, exchange rates, and supplier impact analysis sourced from World Bank LPI and live FX rate APIs.

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.
supplierIdsNoList of supplier identifiers to analyze
baseCurrencyYesBase currency code (ISO 4217) for exposure calculation
riskThresholdNoPercentage threshold for high-risk exposure flagging
targetCurrenciesYesTarget currency codes (ISO 4217) to compare against base

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
dataNo
statusYes
sourcesNo
warningsNo

TDQS

A4.3/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=true, openWorldHint=true, idempotentHint=true. Description adds context by naming data sources (World Bank LPI, live FX rate APIs) and output structure (risk indicators, exchange rates, supplier impact analysis), enhancing transparency beyond annotations alone.

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?

Four clear, front-loaded sentences with no fluff. Every sentence adds value.

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

Completeness5/5

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

Given the presence of output schema and annotations, the description sufficiently covers inputs, outputs, data sources, and intended audience. No gaps identified.

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 good descriptions of all 5 parameters. Description mentions key inputs (supplier IDs, base currency, target currencies) but adds no additional meaning beyond what schema already provides.

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?

Description states specific verb+resource (provides FX exposure dashboard) and target persona (COO). Differentiates from sibling supply chain tools like supplier_esg_audit by focusing on currency risk across suppliers and regions.

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?

Clearly states intended use case for COO tracking currency risk. Does not explicitly state when not to use or point to alternative tools like fx_rate for simple rate lookups.

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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