search_fdic_institutionsSearch FDIC-insured banks and financial institutions by state, name, or city. Returns total assets, deposits, number of offices, charter class, and regulator.
| Name | Required | Description | Default |
|---|---|---|---|
| city | No | City name | |
| name | No | Institution name (partial match) | |
| limit | No | Max results (default 25) | |
| state | No | Two-letter state code (e.g., NY) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the agent knows it's a safe read operation. The description adds the specific return fields (assets, deposits, etc.). However, it does not disclose behavior such as handling of empty results, rate limits, or authentication needs. With annotations covering the safety profile, the description is adequate but not rich.
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: one for the action and filters, one for the return fields. No redundant information; each sentence earns its place.
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?
Given the tool's simplicity (search with optional filters), the description lists the key return fields and constraints (state code length). Annotations provide readOnly and openWorld hints. No output schema, but the description covers the output. Missing details on pagination beyond limit, but it is adequate for most use cases.
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 100% with descriptions for all four parameters. The description restates the search criteria but does not add significant meaning beyond the schema (e.g., partial match behavior or default limit). The description mentions return fields, which are not parameters. Baseline 3 is appropriate.
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 searches FDIC-insured banks/financial institutions by state, name, or city, and lists the returned fields (total assets, deposits, etc.). This distinguishes it from sibling tools which cover different domains.
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 clear context on what to use the tool for (searching FDIC institutions) but does not explicitly state when not to use it or mention alternatives. The sibling tools are in different domains, so confusion is unlikely, but guidance on exclusions is absent.
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.
Each tool has a clear, specific purpose with detailed descriptions that differentiate them. Prefix patterns like get_district_, search_, analyze_, get_, etc., help an agent easily identify the correct tool for a task.
All tool names use a consistent verb_noun or verb_noun_noun pattern with underscores. The naming convention is uniform across the entire set, with no mixing of styles or ambiguous verbs.
With 47 tools, the count is high but justified by the broad scope of civic data analysis. While some agents might find the sheer number overwhelming, the tools are organized into clear categories (district profiles, searches, analyses) that make navigation feasible.
The toolset covers an impressively wide range of domains: legislation, representatives, districts, voting, committees, campaign finance, lobbying, federal spending, regulations, environment, energy, healthcare, housing, disaster, banking, consumer complaints, crime, vehicles, and more. There are no obvious missing operations for a civic data platform.