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) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description contributes additional behavioral context by naming the returned metric families and stating ordering ('most recent quarters first'), which is not present in annotations.
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 two sentences with no filler: action, input, output scope, and ordering are all packed in. Front-loading the main operation makes it immediately scannable.
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 read-only lookup with rich annotations, the description and schema together supply required input, output content, and result ordering. The only gap is the lack of an output schema, so exact field names/units are not specified, and FDIC sibling boundaries are not explicitly drawn.
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%, so both cert and limit already carry meaningful descriptions; the tool description adds no new parameter-level semantics beyond restating CERT's role. Baseline 3 applies because the schema handles parameter documentation.
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 opens with a specific action and resource: 'Get quarterly financial data for a specific bank by CERT number.' It then enumerates the returned metric categories, and bank-level scope separates it from FDIC aggregates and lookup siblings.
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 makes the intended use case explicit: a specific bank identified by CERT, with recent quarters. The schema adds the workflow hint to obtain cert from fdic_search_institutions, but the description does not explicitly name aggregate/disaggregate siblings it should be preferred over.
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.
Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.
The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.
290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.
For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.