Credit union financial performance benchmarks — capital ratios, net interest margin, loan growth, and delinquency rates by asset size. Source: NCUA quarterly call report public data. For credit union CFOs preparing for NCUA exams and board reporting.
| Name | Required | Description | Default |
|---|---|---|---|
| charter_type | No | ||
| asset_size_tier | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds value by disclosing the source (NCUA public data) and the specific metrics covered, giving context about what the tool returns without needing to restate 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences that front-load the primary purpose, follow with the source, and end with the target audience. No filler or redundant details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is sufficient for an agent to understand the tool's niche, but omits any explanation of output structure, time periods, or how to interpret the benchmarks. With no output schema and 0% parameter coverage, the description could do more to round out the agent's understanding, though it covers the core purpose well overall.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% and the description only mentions 'by asset size', which relates to asset_size_tier but provides no detail on charter_type. The enum values in the schema are self-explanatory, but the description does not compensate for the lack of parameter descriptions in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool provides credit union financial performance benchmarks with specific metrics (capital ratios, net interest margin, etc.) and identifies the data source. However, it does not explicitly differentiate from the sibling get_ncua_credit_union_financials, relying on the 'benchmarks' framing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives a concrete use case ('CFOs preparing for NCUA exams and board reporting') which implies when to use it, but offers no exclusions or comparisons to alternative benchmark tools. The context is present but not explicit about when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Multiple tools have overlapping purposes, e.g., get_inflation_benchmark and get_bls_inflation_components, get_commodity_benchmark and get_agricultural_commodity_benchmark. Descriptions provide some differentiation, but many benchmark tools cover similar domains, leading to high potential for misselection.
All tools follow a consistent 'get_' prefix with snake_case nouns, e.g., get_inflation_benchmark, get_ma_multiples_benchmark. No mixing of conventions or irregular naming patterns.
46 tools is excessive for a server focused on financial benchmarks and intelligence. While the domain is broad, many tools could be consolidated. The high count may overwhelm agents and suggests insufficient scoping.
The toolset covers a wide range of financial data—benchmarks, regulatory filings, commodity prices—but lacks granular tools like individual stock prices or sector-specific indices. Some areas (e.g., credit unions) are well-covered, but other common financial operations (e.g., portfolio analytics) are absent.