Get quarterly financial data for a specific bank by CERT number (FDIC Certificate Number). Returns recent quarters of assets, deposits, loans, capital ratios, income, and asset quality metrics. Most recent quarters first.
| Name | Required | Description | Default |
|---|---|---|---|
| cert | Yes | FDIC Certificate Number (get this from fdic_search_institutions) | |
| limit | No | Number of recent quarters (default 4) |
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 establish safety (readOnly, idempotent, non-destructive), and the description adds behavioral detail beyond them: output is ordered with most recent quarters first and covers specific metric families. No contradictions or hidden side effects are disclosed or suggested.
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 compact sentences, front-loaded with action and object, then a tight list of return fields. Every sentence carries useful information with no 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?
Given the simple two-parameter schema and read-only annotations, the description supplies the critical missing pieces: the certification identifier context, time-series nature, field coverage, and ordering. With no output schema, listing the metric families is sufficient for an agent to set expectations.
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?
The schema covers both parameters 100%, so the baseline of 3 applies. The tool description reinforces that cert selects a bank and that 'recent quarters' relates to the limit parameter, but it doesn't add substantial new semantic meaning 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?
Description opens with a specific verb ('Get') and resource ('quarterly financial data for a specific bank by CERT number'), then enumerates the returned metric categories. This is enough to distinguish it from sibling FDIC tools such as fdic_summary or fdic_deposits by scope and content.
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 intended use case is clear: retrieve financials for one bank when a CERT is known. It doesn't list exclusions or point to alternatives, but the 'specific bank' and 'most recent quarters' wording gives a clear contextual trigger.
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.