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mcp-knowledge

by io.github.getgapup

banking_fee_negotiator

Read-onlyIdempotent

As a CFO-focused tool, banking_fee_negotiator analyzes your bank's fee structures (account maintenance, wire transfers, credit lines) and provides data-driven negotiation recommendations. Input your current fees and bank details to receive benchmark comparisons from World Bank and ECB SDW, along with specific levers to reduce costs. Ideal for optimizing treasury operations and improving financial efficiency. Keywords: bank fees, cost optimization, treasury management, financial benchmarking, negotiation strategy.

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.
industryNoIndustry classification (e.g., 'manufacturing', 'retail')
bank_countryYesISO 2-letter country code of the bank
credit_line_feeNoCurrent annual credit line fee percentage
wire_transfer_feeNoCurrent domestic wire transfer fee in USD
international_wire_feeNoCurrent international wire transfer fee in USD
account_maintenance_feeYesCurrent monthly account maintenance fee in USD

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
negotiation_leversNo
credit_line_benchmarkNoIndustry benchmark for credit line fees percentage
wire_transfer_benchmarkNoRegional benchmark for domestic wire transfer fees in USD
international_wire_benchmarkNoRegional benchmark for international wire transfer fees in USD
account_maintenance_benchmarkNoRegional benchmark for account maintenance fees in USD

TDQS

A4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint and idempotentHint. The description adds context about data sources (World Bank, ECB SDW) and output (levers, benchmarks), enriching behavioral understanding without contradicting 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: first explains purpose and input, second describes output and ideal use, third provides keywords. Front-loaded and concise with 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?

Given the existence of an output schema, the description sufficiently covers purpose, input, and output nature (benchmark comparisons and levers). It does not need to detail return values but could mention the output schema existence.

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 descriptions for all parameters. The description merely lists fee types already covered in the schema, adding no new meaning beyond what the schema 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 analyzes bank fee structures and provides negotiation recommendations using benchmarks. It is specific and distinct from sibling tools like 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 Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description says to input current fees and bank details for negotiation recommendations, and mentions it's ideal for optimizing treasury operations, but does not explicitly state when not to use it or provide 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.

Resources