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LiveDataLink

by io.github.blackboxfoundry

usgs_water_realtime

Read-onlyIdempotent

Real-time water data from USGS NWIS streamgages. Filter by site code, state, or parameter (e.g. '00060' = streamflow cfs, '00065' = gage height ft). Useful for flood-stage monitoring, drought tracking, and hydrological research.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sitesNoComma-separated USGS site codes (e.g. '01646500')
state_cdNoTwo-letter state code; returns all active sites in the state
parameter_cdNoUSGS parameter code (default '00060' streamflow). Common: 00060=streamflow, 00065=gage height, 00010=water temp, 00400=pH

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds useful context about the data source and real-time nature, but does not disclose additional behavioral details such as response format, pagination, or data latency.

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 three sentences and front-loads the core purpose, then covers filtering options and use cases. Every sentence earns its place with no redundancy or 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?

For a read-only tool with three optional parameters and thorough schema descriptions, this description is complete enough for an agent to select and invoke it correctly. It provides the source, filter dimensions, parameter code examples, and likely use cases, while annotations cover the safety profile.

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 high, with all three parameters already documented. The description adds value by giving concrete examples with units ('00060' = streamflow cfs, '00065' = gage height ft) and reinforcing the filtering modes, which helps the agent construct valid queries.

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

Purpose4/5

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

The description clearly states that the tool provides 'Real-time water data from USGS NWIS streamgages' and can be filtered by site, state, or parameter. This is a specific resource and scope, but it does not differentiate this tool from similar siblings like water_levels or tide_predictions.

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 provides clear context with 'Useful for flood-stage monitoring, drought tracking, and hydrological research,' giving an agent a sense of when to choose it. However, it does not explicitly name alternative tools or state when not to use it.

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

B3.2/5.0
Disambiguation2/5

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.

Naming Consistency3/5

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.

Tool Count1/5

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.

Completeness4/5

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.

Resources