gapup-mcp

interest_rate

Read-only

Return a precise reference interest rate — the exact figure an agent injects into a treasury, lending, valuation or trading model. Available rates: fed_funds, sofr, us_10y, us_2y, us_3m, ecb_main, euribor_3m. Source: FRED (Federal Reserve Bank of St. Louis). When to use: an agent's computation needs a current benchmark rate as a precise input.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`rate`	Yes	Reference rate name
`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.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`rate`	Yes
`unit`	Yes
`as_of`	Yes
`value`	Yes
`source`	Yes
`series_id`	No
`source_url`	No

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, so the description doesn't need to duplicate that. It adds context about the data source (FRED) and that the rate is precise, but doesn't disclose additional behavioral traits like update frequency or caching.

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 two sentences plus a list, front-loading the action and purpose. Every sentence contributes value with zero waste.

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?

For a simple read-only tool with an output schema, the description provides the key context: exact rates available, source, and use case. It could mention that rates are current/latest, but overall it's adequate.

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 baseline is 3. The description lists the available rates which matches the enum in schema, and mentions 'precise reference interest rate' but doesn't add distinct semantic value beyond what the schema provides for each parameter.

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 'Return a precise reference interest rate' and lists exactly which rates are available (fed_funds, sofr, etc.) and the source (FRED). This distinguishes it from sibling tools like fx_rate or economic_indicator which serve different purposes.

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?

Explicitly states 'When to use: an agent's computation needs a current benchmark rate as a precise input.' This gives clear context, though it does not mention when not to use or 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

C2.5/5.0

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.

Resources

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