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mcp-knowledge

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

A4/5.0
Behavior3/5

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

Annotations already provide readOnlyHint and destructiveHint, so transparency burden is lower. The description adds source (FRED) and that the rate is 'current', but doesn't disclose return format (e.g., percentage vs decimal) or whether rates are delayed.

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?

Two efficient sentences: first states purpose and lists rates, second gives source and usage condition. No extraneous text; front-loaded with key information.

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 and the presence of an output schema, the description covers core aspects (purpose, rates, source, usage). Could mention return format or update frequency, but overall 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 coverage is 100%, so the schema already documents both parameters. The description adds source context and lists rates (matching enum), but does not add significant new meaning beyond the schema for the async 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 states a specific verb and resource ('Return a precise reference interest rate') and includes explicit use cases (treasury, lending, valuation, trading models), clearly distinguishing it from siblings like historical_price_series or fx_rate.

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?

Includes a 'When to use' clause that directly tells the agent when to invoke this tool ('agent's computation needs a current benchmark rate'), but does not explicitly mention when not to use it or list 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.

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