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gapup-mcp

by io.github.getgapup

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
NameRequiredDescriptionDefault
rateYesReference rate name
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.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
rateYes
unitYes
as_ofYes
valueYes
sourceYes
series_idNo
source_urlNo

TDQS

A3.8/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is known. The description adds the source (FRED) and emphasizes the 'precise' nature of the rate, but does not disclose additional behavioral traits such as latency, rate limits, or potential variance. It is consistent with 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 and well-structured: a clear opening sentence, a list of available rates, the source, and a usage note. No wasted words, and the most important information is front-loaded.

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 tool's simplicity (two parameters, one enum) and the presence of an output schema, the description covers the essential aspects: purpose, rates, source, and when to use. It does not explain the async behavior, but the schema already does, so this is adequately 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?

The schema already covers both parameters with descriptions, including a detailed async explanation. The tool description lists the enum values for 'rate', which adds no new meaning beyond the schema, and it does not explain the semantics of each rate. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool returns a precise reference interest rate for use in financial models, and lists the specific rates available. It does not explicitly distinguish from sibling tools like fx_rate or economic_indicator, but the focus on benchmark interest rates and the FRED source provides sufficient clarity.

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 includes a 'When to use' clause specifying that the tool is for when an agent needs a current benchmark rate as a precise input. This gives clear context for invocation, though it does not mention exclusions 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

C2.4/5.0
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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