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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

A4.2/5.0
Behavior4/5

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

Annotations already provide read-only, idempotent, and open-world hints. The description adds meaningful behavioral context by disclosing data sources (ECB FX rates, World Bank remittance price data) and what outputs to expect (cost breakdown, FX rates, settlement time estimates). No contradictions with 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?

The description is three sentences, front-loaded with the core purpose, and every sentence adds value (purpose, input/output, data sources). No redundant or vague wording.

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?

Given the tool complexity, schema coverage, and presence of an output schema, the description is complete enough. It explains what the tool does, key inputs, outputs, and data sources. It could mention optional currency inputs explicitly, but those are already in the schema.

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 description coverage is 100%, so the baseline is 3. The description mentions the key required parameters (source/destination countries, amount) but adds little beyond that—for instance, it does not elaborate on currency parameters or async behavior, which are already documented in the 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?

The description clearly states the tool's function: comparing cross-border payment rail costs (SWIFT, SEPA, local ACH, stablecoins) including FX fees and settlement times. This is a specific verb+resource+scope that distinguishes it from related tools like fx_rate or x402_payment_flow_analyzer.

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 gives clear context for use: 'Ideal for optimizing international payment strategies and reducing transaction expenses.' It does not explicitly name alternatives or exclusions, but the purpose is unambiguous enough for an agent to decide when to invoke it.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.