CDC NORS foodborne / waterborne / enteric outbreak reports (iezt-77pi). Returns outbreak date, state, etiology, illnesses, hospitalizations, deaths, and implicated food/exposure.
| Name | Required | Description | Default |
|---|---|---|---|
| year | No | Outbreak year | |
| limit | No | Max rows (default 50) | |
| state | No | Full state name | |
| etiology | No | Causative agent (e.g. 'Salmonella', 'Norovirus', 'E. coli') |
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, non-destructive behavior. The description adds value by enumerating the returned data fields (date, state, etiology, illnesses, hospitalizations, deaths, exposure), which is especially helpful in the absence of an output schema. It does not disclose pagination or default-limit behavior, but the annotations cover the safety profile.
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?
A single, front-loaded sentence identifies the dataset, scope, and return fields with zero filler. Every word contributes to the agent's ability to understand and invoke the tool.
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 read-only query tool with four optional parameters and no output schema, the description plus schema covers the essential invocation details: dataset, filters, returned fields, and default limit. It stops short of explaining filtering behavior or result-set expectations, but the tool is simple enough that these are minor gaps.
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 coverage is 100%, with each parameter (year, limit, state, etiology) already having a description in the input schema. The tool description names some of these concepts (state, etiology) but does not add meaning beyond what the schema provides, so the baseline score of 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 names a specific resource (CDC NORS foodborne/waterborne/enteric outbreak reports), the dataset identifier (iezt-77pi), and the exact fields returned. This clearly distinguishes the tool from the many other cdc_* siblings by domain and dataset.
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 is implied by 'CDC NORS ... outbreak reports' and the returned fields, but the description does not explicitly state when to choose this tool over alternatives like cdc_dataset_query or other CDC health datasets. No exclusions or alternative routing are provided.
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.