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TerraMCP

ea_groundwater_stations

Read-only

Find EA Hydrology groundwater monitoring boreholes near a UK point. Returns station IDs, aquifer, datum and measures for ea_hydro_series(property=groundwaterLevel). OGL v3; absence is not evidence of no groundwater risk. Returns source data and provenance; supported hydrological units are normalised to SI. TerraMCP is a Wrench AI service. Open data: no API key or signup required. Anonymous limits: 60 calls/hour and 300/day per IP and session; keys offer higher limits, with anonymous fallback after monthly quota. Source licence warnings apply.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
latYesConfirmed WGS84 latitude in decimal degrees; pair with lon.
lonYesConfirmed WGS84 longitude in decimal degrees; pair with lat.
limitNoMaximum number of results to return; bounded by this tool's schema.
radius_kmNoSearch radius in kilometres; spatial tools use a bounding box around the point.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Goes well beyond the annotations (readOnly/openWorld/non-destructive), disclosing the OGL v3 licence, the critical caveat that absence is not evidence of no groundwater risk, SI normalisation, provenance handling, and concrete anonymous rate limits (60/hour, 300/day) plus key/quota fallback. This is exactly the operational context annotations cannot convey.

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?

Front-loads purpose and return contents, then adds genuinely useful caveats and rate limits. However, filler such as 'TerraMCP is a Wrench AI service' and the vague 'Source licence warnings apply' consume space without helping the agent select or invoke the tool.

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 burden well by naming the fields returned (station IDs, aquifer, datum, measures), their downstream use, and provenance/normalisation behavior. Combined with annotations covering the safety profile, an agent has everything needed to call it correctly.

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 description coverage is 100%, so lat, lon, limit and radius_km are fully documented in the schema with ranges and defaults. The description only gestures at 'near a UK point' and adds no format or constraint detail beyond what the schema already provides, so the baseline 3 applies.

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?

States a specific verb (Find), resource (EA Hydrology groundwater monitoring boreholes), and scope (near a UK point), and ties the resource to the sibling ea_hydro_series(property=groundwaterLevel) it feeds. An agent can distinguish this station-locator from the series-reading siblings without opening either schema.

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?

Establishes clear context for use and points to the downstream consumer (ea_hydro_series with property=groundwaterLevel), which effectively tells the agent when this tool is the right entry point. It stops short of naming an explicit alternative to use instead or stating a when-not-to-use condition.

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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