Get detailed financial information for a specific FDIC-insured institution.
Returns Call Report data for the most recent reporting period (2024-06-30)
including assets, deposits, income, classification, and regulator.
Args:
cert: FDIC certificate number uniquely identifying the institution.
| Name | Required | Description | Default |
|---|---|---|---|
| cert | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the tool returns Call Report data for a specific reporting period and lists included categories (assets, deposits, etc.), which is transparent for a read-only tool.
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 with only 4 sentences, each adding value: purpose, return data summary, parameter explanation. No unnecessary words.
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?
For a single-parameter tool with an output schema, the description covers the purpose, return data scope, and parameter meaning completely. No gaps.
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 compensates by explaining that 'cert' is an FDIC certificate number uniquely identifying the institution, adding essential context beyond 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 uses a specific verb ('Get') and resource ('detailed financial information for a specific FDIC-insured institution'), which clearly distinguishes it from siblings like 'get_bank_failures' or 'search_institutions'.
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 clearly states the tool's purpose and the required parameter (cert), implying use when detailed financial data is needed. However, it does not explicitly mention when not to use this tool or provide direct comparisons to sibling tools.
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.