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Daily Values

daily_values
Read-onlyIdempotent

Daily-values time series for a single USGS site + parameter over a date range. Returns one statistic per day (default mean). Useful for trends, hydrographs, and historical comparison. Common statCd: 00003 = mean, 00001 = max, 00002 = min. Common parameter codes: 00060 = discharge (ft³/s), 00065 = gage height (ft), 00010 = water temperature (°C). Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
siteYesA single USGS site number, e.g. "01646500".
end_dateYesEnd date, YYYY-MM-DD.
stat_codeNoDaily statistic code (default "00003" = mean; 00001 = max, 00002 = min).
start_dateYesStart date, YYYY-MM-DD.
parameter_codeNoUSGS parameter code (default "00060" = discharge/streamflow).

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, so the safety profile is covered. The description adds useful behavioral context beyond annotations: it returns one statistic per day, defaults to mean, and is 'Keyless', meaning no authentication is required.

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

Conciseness5/5

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

The description is compact and efficiently organized: core behavior first, then use cases, then common codes, then the keyless auth note. Each sentence earns its place with no filler or redundant restatement of the schema.

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?

For a tool with 100% schema coverage, robust annotations, and no nested objects, the description covers what is most needed: return granularity, defaults, commonly used codes, and authentication. There is no output schema, but the description sufficiently explains the returned shape (one statistic per day) for an agent to invoke the tool correctly.

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

Parameters4/5

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

Schema coverage is 100%, so the schema already documents each parameter. The description adds value by enumerating common stat codes and parameter codes with units (e.g., 00060 = discharge ft³/s) and stating defaults, which helps an agent select correct values without external lookup.

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?

The description states exactly what the tool does: returns a daily-values time series for a single USGS site and parameter over a date range, with one statistic per day. The scope ('single USGS site + parameter', 'date range') clearly distinguishes it from siblings like current_conditions or find_sites, even without naming them.

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

Usage Guidelines4/5

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

The description explicitly says the tool is 'useful for trends, hydrographs, and historical comparison', giving clear context for when to use it. It does not explicitly name sibling alternatives or state when not to use it, but the single-site, date-range framing implies the boundaries well enough.

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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TDQS

A4/5.0
Disambiguation3/5

Several tools overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve as query routers, with the beta variant currently identical to the stable one. However, most other tools have clearly distinct purposes (memory, subscriptions, prediction market analytics), and the detailed descriptions help differentiate them.

Naming Consistency4/5

All tool names use snake_case and are descriptive, with consistent domain prefixes like pipeworx_ for meta tools and polymarket_ for prediction markets. Some names mix noun-phrase and verb-noun patterns (e.g., ai_visibility_check vs. resolve_entity), but the overall style is predictable and readable.

Tool Count2/5

With 31 tools, the server exceeds the 25-tool threshold for a coherent set. While the broad scope (data querying, prediction markets, memory, subscriptions, AI visibility) justifies many tools, the sheer number creates cognitive load and makes selection harder for agents.

Completeness4/5

The tool surface is quite comprehensive for its domains: querying has ask_pipeworx, grounded answer, deep research, entity profiles, comparisons, and claim validation; prediction markets have research, arbitrage, edge tracking, and fill risk; memory and subscription lifecycles are covered. Minor gaps exist (e.g., no subscription update) but are workable.