CDC drug overdose deaths by state and indicator (9j2v-jamp). 12-month rolling counts. Useful for opioid/fentanyl/stimulant policy research and treatment-program siting.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 50) | |
| indicator | No | Drug class (e.g. 'Opioids (T40.0-T40.4,T40.6)', 'Synthetic opioids, excl. methadone (T40.4)') | |
| state_name | No | Full state name |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds a meaningful behavioral detail beyond annotations: the data are '12-month rolling counts', which affects interpretation and distinguishes this from point-in-time death statistics. Annotations already convey safety (read-only, idempotent, non-destructive), so the description does not need to repeat those.
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 compact and front-loaded: the first sentence states the dataset and scope, the second adds the rolling-count behavior, and the third gives downstream use cases. There is minimal filler, though a more explicit action verb could improve it slightly.
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 parameters and rich annotations, the description is adequate. However, it does not address default limits, date range behavior, return format, or when to choose this over the generic cdc_dataset_query sibling, leaving some interpretation to the agent.
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 all three parameters. The description only echoes 'state and indicator' without adding new parameter-level meaning, such as accepted formats or relationships between filters. Baseline 3 is appropriate.
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 identifies the specific dataset ('CDC drug overdose deaths by state and indicator'), includes the Socrata dataset ID, and names the two main dimensions. It lacks an explicit verb like 'retrieve' or 'query', but the subject matter and scope are clear enough to distinguish it from sibling CDC tools.
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 gives a use context ('opioid/fentanyl/stimulant policy research and treatment-program siting') but does not explicitly say when to prefer this tool over alternatives such as cdc_dataset_query or cdc_weekly_deaths_by_state. Usage is only implied through topic specificity.
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.