Industry-level summary financials. Returns year-by-year aggregates across all FDIC-insured institutions, optionally filtered to a single state. Useful for macro banking-sector analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of years (default 20) | |
| state | No | Two-letter state code (omit for national) |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, and non-destructive behavior. The description adds useful behavioral context beyond that: it returns year-by-year aggregates and supports optional state-level filtering. It does not detail exact metrics returned or ordering, but the annotations lower the bar for additional safety-related disclosure.
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 tight sentences: the first front-loads the core purpose and scope, the second gives the primary use case. There is no redundant wording or filler, and every clause contributes to tool selection and invocation.
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 simple two-parameter tool with no required parameters and full schema coverage, the description gives enough context to select and call it correctly: what it returns, at what aggregation level, and how state filtering works. The absence of an output schema means the actual financial fields are not enumerated, but 'summary financials' is a reasonable high-level contract.
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 100%: 'limit' and 'state' are both documented with defaults and meaning. The description only echoes the aggregation and state-filter idea at a higher level, adding little parameter nuance beyond what the schema already provides, so the baseline 3 applies.
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 states a specific resource ('Industry-level summary financials') and a clear behavior: returning year-by-year aggregates across all FDIC-insured institutions, optionally by state. This differentiates it from institution-level sibling tools like fdic_financials and fdic_search_institutions by emphasizing aggregate, sector-wide scope.
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 explicitly frames the tool as 'useful for macro banking-sector analysis,' providing a clear context for when to use it. It does not name alternatives or state when not to use it, so it stops short of full routing guidance, but the context is clear enough for most agents.
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.
Several tool clusters overlap heavily—company due-diligence and risk tools (counterparty_risk_score, company_trust_check, entity_dossier, issuer_diligence_dossier, resolve_entity, entity_resolve), carrier vetting tools, sanctions screening tools, and recall tools all have subtle boundary distinctions. While descriptions are detailed, an agent navigating 294 tools will frequently struggle to pick the right one.
Most tools follow a readable snake_case domain-prefix pattern (fdic_, edgar_, sanctions_, congress_), which helps. However, verb placement is inconsistent—search_available_datasets vs cdc_dataset_query, resolve_entity vs entity_resolve—and synonyms like search, lookup, get, detail, fetch, and status are used interchangeably.
294 tools is an extreme number for a single MCP server, far beyond what an agent can reliably hold in context or select from accurately. The presence of tool-group discovery helpers mitigates but does not solve the fundamental scale problem.
The data breadth is genuinely extensive, covering finance, health, legal, real estate, transportation, energy, cyber, education, and many other domains, often with generic query fallbacks. Still, some capabilities are shallow or incomplete—package tracking stops at a link, property tools are demo-only in places, and caselaw coverage is limited—so it is not a fully complete surface.