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Wastewater National Trend

wastewater_national_trend
Read-onlyIdempotent

National CDC NWSS wastewater viral activity level (WVAL) for SARS-CoV-2, influenza A, or RSV — the leading public signal for near-term respiratory infection and test-demand trends, published weekly (Fridays) from ~800+ US sewershed sites. Returns the latest week's national level (Very Low/Low/Moderate/High/Very High, population-weighted across reporting sites), the category breakdown, and a week-over-week trend. Example: wastewater_national_trend({"pathogen":"SARS-CoV-2"}).

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "pathogen": "SARS-CoV-2"
    },
    "name": "wastewater_national_trend"
  }
}

Input Schema

NameRequiredDescriptionDefault
weeksNoTrailing weeks of history to include (default 4, max 12).
pathogenNoPathogen: "SARS-CoV-2" (aliases: covid, covid-19), "Influenza A" (aliases: flu, influenza), or "RSV". Default SARS-CoV-2.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnly/idempotent/openWorld and non-destructive, so safety is covered. The description adds genuinely useful operational context the annotations cannot: weekly Friday publication cadence, ~800+ reporting sites, population-weighted aggregation, and the freshness implication that data may lag within a week.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single dense sentence plus an example, front-loaded with the resource and signal value. Some enumeration of return fields is slightly redundant but every clause is informative; no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the return contract well: latest national level with its five-level scale, the category breakdown, week-over-week trend, and the population-weighting semantics. Nothing essential for correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the schema already documents defaults, aliases, and the max of 12 weeks, so the baseline is 3. The description's example call and pathogen naming add only marginal value beyond what the schema fields already provide.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Names a specific resource (national CDC NWSS wastewater viral activity level) with explicit scope (national, population-weighted, three named pathogens), and the 'national' framing implicitly separates it from wastewater_state_trend. An agent can tell what this returns without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It implies when the tool is useful ('leading public signal for near-term respiratory infection and test-demand trends') and includes a concrete example call, but never states when to prefer it over wastewater_state_trend or any exclusion conditions. Usage is inferable, not stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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