gapup-mcp

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

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.
`amount`	Yes	Transaction amount in source currency
`source_country`	Yes	ISO 3166-1 alpha-2 country code of payment origin
`source_currency`	No	ISO 4217 currency code of source amount
`destination_country`	Yes	ISO 3166-1 alpha-2 country code of payment destination
`destination_currency`	No	ISO 4217 currency code of destination amount

Output Schema

TableJSON Schema

Name	Required	Description	Default
`amount`	No
`status`	Yes
`fx_rate`	No
`sources`	No
`warnings`	No
`total_cost`	No
`source_country`	No
`settlement_time`	No
`source_currency`	No
`destination_country`	No
`destination_currency`	No

TDQS

A4.4/5.0

Behavior5/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. Description adds value by specifying data sources (ECB FX rates, World Bank) and outputs (cost breakdown, FX rates, settlement times), which are 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.

Conciseness5/5

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

Three sentences, front-loaded with user role and action, then input/output, data sources, and use case. No wasted words.

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?

Description covers purpose, inputs, outputs, and data sources. It omits potential limitations or error conditions, but for a read-only analysis tool with an output schema, this 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% with clear param descriptions. Description mentions source/destination countries and amount, linking them to purpose, but adds no additional semantic depth beyond schema.

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 clearly states the tool compares cross-border payment rail costs including FX fees and settlement times, specifying rails like SWIFT, SEPA, local ACH, and stablecoins. It distinguishes from siblings like fx_rate and treasury_optimizer by focusing on cost comparison across multiple rails.

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?

Description implies usage context: for CFOs optimizing international payment strategies, with input countries and amount. It lacks explicit exclusions or alternatives but provides clear context.

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.

Resources

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