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mcp-knowledge

by io.github.getgapup

payment_rails_cost_analyzer

Read-onlyIdempotent

As a CFO, compare cross-border payment rail costs (SWIFT, SEPA, local ACH, stablecoins) with FX conversion fees and settlement times. Input source/destination countries and amount, receive cost breakdown, FX rates, and settlement time estimates. Uses ECB FX rates and World Bank remittance price data for accurate cost analysis. Ideal for optimizing international payment strategies and reducing transaction expenses.

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.
amountYesTransaction amount in source currency
source_countryYesISO 3166-1 alpha-2 country code of payment origin
source_currencyNoISO 4217 currency code of source amount
destination_countryYesISO 3166-1 alpha-2 country code of payment destination
destination_currencyNoISO 4217 currency code of destination amount

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
amountNo
statusYes
fx_rateNo
sourcesNo
warningsNo
total_costNo
source_countryNo
settlement_timeNo
source_currencyNo
destination_countryNo
destination_currencyNo

TDQS

A4.5/5.0
Behavior4/5

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

Annotations (readOnlyHint, openWorldHint, idempotentHint) are consistent with the description. The description adds valuable context about data sources (ECB FX rates, World Bank data) that goes beyond structured fields. No contradictions.

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 sentences with no fluff. The first sentence captures the core purpose, followed by input/output, data sources, and ideal use. Every sentence earns its place.

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 tool has an output schema, the description adequately explains what the tool returns (cost breakdown, FX rates, settlement times). For a read-only analytical tool with 6 parameters and clear annotations, this description is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All parameters have schema descriptions (100% coverage), so baseline is 3. The description adds meaning by explaining how the inputs (source/destination countries, amount) are used to produce output. It does not mention optional currency parameters, which is a minor gap.

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 specifies the tool's function: comparing cross-border payment rail costs with FX fees and settlement times. It uses specific verbs ('compare', 'input', 'receive') and identifies distinct resources (SWIFT, SEPA, local ACH, stablecoins). This distinguishes it from sibling tools like fx_rate or treasury_optimizer.

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?

The description states the ideal use case ('optimizing international payment strategies and reducing transaction expenses') but does not explicitly advise when not to use the tool or mention alternatives. However, the context is clear enough for an agent to decide.

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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