COVID-19 vaccination coverage by US county (8xkx-amqh). Returns booster + primary series percentages over time. Useful for public-health gap analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 50) | |
| recip_state | No | Two-letter state code (e.g. 'CA') | |
| recip_county | No | County name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already cover read-only, open-world, idempotent, and non-destructive behavior, lowering the burden. The description adds that it returns percentages over time, which is useful, but it does not describe output shape, pagination, temporal resolution, or how county/state filtering behaves in practice.
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?
Three concise sentences with no filler. The main content and dataset identifier are front-loaded, and the use-case sentence adds value without bloat.
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 read-only query tool with fully documented optional parameters and safety annotations, the description is largely complete. It conveys subject matter, metrics, temporal aspect, and suggested application. It could improve by noting the data source or whether output is a time series by date, but this is a minor gap given the schema and annotations.
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 the schema already documents limit, recip_state, and recip_county. The description does not add parameter-level meaning, but it does not need to; it correctly stays at the baseline.
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 returns COVID-19 vaccination coverage by US county, identifies the dataset ID, and specifies the metrics (booster + primary series percentages over time). This is specific enough to differentiate it from other CDC tools like cdc_drug_overdose_deaths or cdc_flu_surveillance.
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 offers a use case ('public-health gap analysis'), giving implied context for when the tool is relevant. However, it does not explicitly state when to prefer this tool over closely related alternatives such as cdc_dataset_query or other cdc_* tools, so guidance is incomplete.
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.