CDC FluView state-level influenza surveillance (vh55-3he6). Returns weekly ILI (influenza-like illness) activity levels per state.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 50) | |
| season | No | Flu season (e.g. '2023-24') | |
| statename | No | Full state name |
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 cover safety (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the description's burden is reduced. It adds useful context about scope (state-level, weekly) and the dataset, but does not describe output format, pagination, or behavior with empty filters. No contradiction with 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 a single sentence that front-loads the source and the return value, with no filler and no repetition of schema fields. The dataset ID is useful and unobtrusive.
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 tool with zero required parameters and full schema coverage, the description is nearly complete. It states what is returned and at what granularity. However, since there is no output schema, it could be slightly clearer about the form of 'activity levels' (e.g., categorical vs. numeric) and default behavior when no filters are supplied.
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 input schema already describes all three parameters at 100% coverage (limit, season, statename). The description does not add meaningful parameter-level detail 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 source and resource ('CDC FluView state-level influenza surveillance') and states a clear action with output ('Returns weekly ILI activity levels per state'). It distinguishes itself from sibling CDC tools by focusing specifically on influenza-like illness, and the dataset ID (vh55-3he6) adds precision.
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 implies when this tool is relevant: any query about state-level flu/ILI activity. However, it does not explicitly state when to prefer this over siblings such as cdc_dataset_query or cdc_weekly_deaths_by_state, nor does it provide exclusions or alternative routing guidance.
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.