Search FDIC-insured financial institutions by name.
Returns matching banks with key financial data from the most recent
Call Report (2024-06-30), sorted by total assets descending.
Args:
search: Institution name or partial name to search for.
limit: Maximum number of results to return (default 10, max 100).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| search | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description transparently discloses return data is from the most recent Call Report (2024-06-30) and sorted by total assets descending. It implies a read-only operation via 'search' and 'returns', but does not explicitly state non-destructiveness or authentication requirements.
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 concise and well-structured using a docstring format with an Args section. It contains no superfluous information, though the style is slightly formal.
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 two parameters, no enums, and presence of an output schema, the description covers purpose, parameters, and key behavioral details. It omits error cases and explicit read-only assurance, but these are minor relative to overall completeness.
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?
Despite 0% schema description coverage, the Args section fully explains both parameters: search is an institution name or partial name, and limit has a default and maximum. This adds substantial meaning beyond the bare property titles in the input 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 searches FDIC-insured institutions by name and returns matching banks with key financial data sorted by total assets descending. This specific verb and resource combination, along with the search scope, distinguishes it from siblings like get_institution (which likely retrieves a single institution) and get_bank_failures.
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 implies usage for searching institutions by name but does not provide explicit when-to-use or when-not-to-use guidance against siblings. It lacks selection criteria such as 'use this instead of get_institution when you need a list'.
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 clearly distinct purpose targeting different FDIC data domains: bank failures, county branches, institution details, institution branches, and institution search. There is no overlap in functionality, and the descriptions clearly differentiate between failure records, branch data, and institution information.
All tools follow a consistent verb_noun pattern with clear, descriptive names: get_bank_failures, get_county_branches, get_institution, get_institution_branches, and search_institutions. The naming convention is uniform throughout the set, using snake_case and appropriate verbs (get/search) that match the action.
With 5 tools, this server is well-scoped for its FDIC banking data domain. Each tool serves a specific, non-overlapping function, covering key aspects like failures, branches, institution details, and search. The count is neither too sparse nor bloated, fitting typical MCP server ranges.
The tool set provides comprehensive coverage for querying FDIC data, including failures, branches, and institution details with search capabilities. A minor gap exists in update or administrative functions (e.g., no tools for modifying data), but this is reasonable for a read-only data server, and agents can fully navigate the available information.