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gapup-mcp

by io.github.getgapup

syndicated_loan_pricing_benchmark

Read-onlyIdempotent

Provides CFOs with peer benchmarking for syndicated loan pricing by comparing current loan terms against market data from Tradeweb and FRED. Inputs include loan amount, tenor, credit rating, and currency. Outputs structured pricing benchmarks with spread, yield, and fee comparisons. Ideal for quick validation of loan competitiveness or negotiation preparation.

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.
tenorYesLoan tenor (e.g., '5Y', '3Y')
regionNoRegion for benchmarking (e.g., 'US', 'EU')
currencyYesCurrency code (e.g., 'USD', 'EUR')
loanAmountYesLoan amount in millions
creditRatingYesBorrower credit rating (e.g., 'BBB', 'BB+')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
benchmarksNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds useful context about data sources (Tradeweb, FRED) and output contents (spread, yield, fee comparisons), which goes beyond the annotation signal. No contradictions or missing critical behavioral caveats are present.

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 compact: three sentences, with the first sentence front-loading the core purpose, the second describing inputs/outputs, and the third noting target use cases. Every sentence earns its place with no 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 output schema, rich annotations, and complete schema descriptions, the description sufficiently covers purpose, inputs, and use cases. It omits optional parameters like region, but these are already documented in the schema and do not hinder selection or invocation.

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 schema fully documents each parameter. The description mentions the required inputs (loan amount, tenor, credit rating, currency) but adds no additional meaning or constraints beyond what the schema already provides.

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 it 'Provides CFOs with peer benchmarking for syndicated loan pricing' and specifies the comparison against Tradeweb and FRED data. This distinguishes it from sibling tools like syndicated_loan_covenant_breach_alert or generic pricing strategists.

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?

It gives clear context for use: 'Ideal for quick validation of loan competitiveness or negotiation preparation.' However, it does not explicitly mention when not to use it or name alternative tools, so it stops short of a full 5.

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.

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