mcp-knowledge

trade_finance_eligibility

Read-onlyIdempotent

Evaluates trade finance eligibility for CFOs by analyzing counterparty risk and jurisdiction using World Bank and BIS data. Inputs include counterparty country code (ISO 3166-1 alpha-3) and industry sector. Returns risk scores, eligibility flags, and financing terms. Ideal for assessing letters of credit, export credit agency guarantees, and other trade finance instruments. Keywords: trade finance, counterparty risk, jurisdiction risk, letters of credit, ECA guarantees.

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.
`industrySector`	Yes
`annualTradeVolumeUSD`	No
`counterpartyCountryCode`	Yes
`counterpartyCreditRating`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`eligibility`	No
`financingTerms`	No
`countryRiskScore`	No
`maxFinancingAmountUSD`	No
`recommendedInstruments`	No

TDQS

A4.2/5.0

Behavior4/5

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

Annotations already indicate read-only, open-world, and idempotent behavior. The description adds value by detailing return values (risk scores, eligibility flags, financing terms) and data sources, which enhances transparency 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?

The description is concise (4 sentences) and well-structured, starting with the core purpose, then inputs, outputs, use cases, and keywords. Every sentence adds value without redundancy.

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 existence of an output schema, the description covers the main aspects: purpose, inputs, outputs, and use cases. It could mention processing time or limitations, but overall it provides sufficient context.

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?

With only 20% schema description coverage, the description compensates partially by naming two key inputs and their roles. However, it omits the annualTradeVolumeUSD and counterpartyCreditRating parameters, leaving ambiguities for the agent.

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 purpose with a specific verb ('Evaluates') and resource ('trade finance eligibility'). It distinguishes itself from sibling tools by mentioning specific use cases (letters of credit, ECA guarantees) and data sources (World Bank, BIS).

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 provides clear context on when to use the tool, citing ideal applications like letters of credit and export credit agency guarantees. However, it does not explicitly state when not to use it or compare it to alternative tools.

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.

trade_finance_eligibility

Input Schema

Output Schema

TDQS

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TDQS

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