gapup-mcp

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

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.
`supplierIds`	No	List of supplier identifiers to analyze
`baseCurrency`	Yes	Base currency code (ISO 4217) for exposure calculation
`riskThreshold`	No	Percentage threshold for high-risk exposure flagging
`targetCurrencies`	Yes	Target currency codes (ISO 4217) to compare against base

Output Schema

TableJSON Schema

Name	Required	Description	Default
`data`	No
`status`	Yes
`sources`	No
`warnings`	No

TDQS

B3.2/5.0

Behavior3/5

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

Annotations already indicate readOnlyHint, idempotentHint, and openWorldHint. The description adds that the tool uses real-time data from World Bank LPI and live FX APIs, which is useful context. However, no further behavioral traits (e.g., rate limits, caching) are disclosed.

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?

Two sentences, no wasted words. Front-loaded purpose and persona description. Could be more structured with bullet points but still concise and efficient.

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?

Given the tool complexity (5 parameters, many siblings), the description covers core purpose and data sources but omits mentioning the riskThreshold and async parameters, which are part of the schema. Output schema exists so return values are covered.

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 descriptions cover 100% of parameters, so baseline is 3. The description repeats parameter info (supplier IDs, base currency, target currencies) but does not add new meaning beyond the schema. It groups them but no additional semantics.

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 it provides an FX exposure dashboard for supply chain monitoring, listing inputs and outputs. However, it does not explicitly distinguish from sibling tools like fx_rate or working_capital_fx_hedge_optimizer beyond the dashboard focus.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description identifies the COO persona but provides no explicit guidance on when to use this tool versus alternatives. No 'use when' or 'avoid when' criteria, and no mention of prerequisites or limitations.

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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