Bank regulatory capital and financial performance benchmarks — CET1, Tier 1 leverage, NIM, efficiency ratio, charge-off rates, and loan-to-deposit ratio by asset size tier. Source: FDIC call report public aggregates. For bank CFOs, risk officers, and bank analysts.
| Name | Required | Description | Default |
|---|---|---|---|
| bank_type | No | ||
| asset_size_tier | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is clear. The description adds useful context by citing the FDIC call report as a public data source and listing the benchmark metrics, but it does not describe return format, pagination, or other behavioral details, offering only modest value beyond 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?
The description is two sentences and roughly 30 words. It front-loads the core purpose and metrics, then adds source and audience. Every phrase adds value; there is no redundancy or filler.
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?
Combined with the input schema and annotations, this description provides enough context for tool selection: domain, metrics, asset-size tiering, and data source. It does not explain the return shape, but no output schema is present and the metric list gives a reasonable picture for a read-only benchmark tool. It could be improved by naming sibling alternatives.
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 description coverage is 0%, so the description must compensate, but it only references 'by asset size tier,' which maps to the required parameter. It does not explain the enum values (e.g., community_under_1b) or the optional bank_type parameter, leaving parameter semantics under-specified beyond the schema's enum names.
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 opens with 'Bank regulatory capital and financial performance benchmarks' and enumerates specific metrics (CET1, Tier 1 leverage, NIM, efficiency ratio, charge-off rates, loan-to-deposit ratio) 'by asset size tier.' This clearly establishes the tool's scope and differentiates it from sibling tools like credit union or AML benchmarks.
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?
The description provides no explicit when-to-use guidance or mentions of alternatives. The target audience ('bank CFOs, risk officers, bank analysts') implies a use case, but it does not explain when to choose this tool over close siblings like get_bank_financial_intelligence or get_credit_union_benchmark.
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.