gapup-mcp

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

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.
`industry`	No	Industry classification (e.g., 'manufacturing', 'retail')
`bank_country`	Yes	ISO 2-letter country code of the bank
`credit_line_fee`	No	Current annual credit line fee percentage
`wire_transfer_fee`	No	Current domestic wire transfer fee in USD
`international_wire_fee`	No	Current international wire transfer fee in USD
`account_maintenance_fee`	Yes	Current monthly account maintenance fee in USD

Output Schema

TableJSON Schema

Name	Required	Description
`status`	Yes
`sources`	No
`warnings`	No
`negotiation_levers`	No
`credit_line_benchmark`	No	Industry benchmark for credit line fees percentage
`wire_transfer_benchmark`	No	Regional benchmark for domestic wire transfer fees in USD
`international_wire_benchmark`	No	Regional benchmark for international wire transfer fees in USD
`account_maintenance_benchmark`	No	Regional benchmark for account maintenance fees in USD

TDQS

A4.1/5.0

Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, and idempotentHint. The description adds that the tool uses World Bank and ECB SDW data for benchmarks and provides specific cost-reduction levers, which is consistent and adds valuable behavioral context beyond the annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

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

The description is a single paragraph, front-loaded with the core purpose. It is efficient but could be slightly more concise by removing redundant marketing language. Overall, it is well-structured and clear.

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 presence of an output schema (not shown but indicated), the description need not detail return values. It covers the tool's purpose, input requirements, data sources, and use case. For a read-only analytical tool, this is sufficiently complete.

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%, so the input schema already fully describes each parameter. The description repeats some parameter names (account maintenance, wire transfers, credit lines) but does not add significant new meaning beyond what the schema provides. Baseline score of 3 is appropriate.

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 is a CFO-focused analyzer for bank fee structures, providing negotiation recommendations. It uses specific verbs ('analyzes', 'provides') and distinguishes itself from siblings by being narrowly focused on bank fees and treasury optimization.

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 explains when to use the tool (input current fees and bank details to get benchmark comparisons and negotiation levers). It doesn't explicitly state when not to use it, but the niche scope implies it's for bank fee negotiation, which is clear given no closely related sibling 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

C2.5/5.0

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.

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