TerraMCP

wq_measurements

Read-only

Get bounded EA water-quality observations for a sampling_point notation, dates and optional determinand code. Preserves reported units and detection-limit qualifiers (OGL v3). 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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "sampling_point": "<sampling_point>",
      "start": "<start>",
      "end": "<end>"
    },
    "name": "wq_measurements"
  }
}

Input Schema

Name	Required	Description
`end`	Yes	End date in YYYY-MM-DD; must be no earlier than start, at most 366 days apart.
`limit`	No	Maximum number of results to return; bounded by this tool's schema.
`start`	Yes	Start date in YYYY-MM-DD; must be no later than end, at most 366 days apart.
`determinand`	No	Optional numeric EA determinand code to select the measured substance or property.
`sampling_point`	Yes	Sampling-point notation returned by wq_sampling_points.

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 17, 2026

TDQS

A4.2/5.0

Behavior5/5

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

With annotations covering only the safety profile (readOnly, non-destructive, open-world), the description contributes substantial extra behavior: unit preservation and detection-limit qualifiers are retained, hydrological units are normalised to SI, provenance is returned, no API key is needed, and explicit anonymous rate limits (60/hour, 300/day) with key-based fallback are disclosed. That is well beyond what the structured fields 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?

The purpose and behavioural facts are front-loaded before the service branding, rate limits and licence boilerplate. Slightly bulky with the TerraMCP/OGL/licence sentences, but each of those carries operational information an agent can act on.

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?

There is no output schema, yet the description explains what comes back (source data plus provenance, preserved units, qualifiers, SI normalisation). Combined with the full parameter documentation and disclosed auth/rate limits, 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% and each parameter (start, end, limit, determinand, sampling_point) is fully documented in the schema, so the 3 baseline applies. The description restates the parameter roles in prose but adds no format, constraint or defaulting detail that the schema does not already carry.

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 (Get), a specific resource (EA water-quality observations) and its scoping inputs (sampling_point, dates, determinand code). It is clearly distinguishable from siblings like wq_sampling_points, nrfa_get_timeseries and nrw_readings by naming the EA source and the bounded observation scope.

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?

Usage is implied through the scoping inputs and the referenced sampling_point notation, and the 'bounded' framing hints at date-range constraints. However, the description never says when to prefer this over sibling series tools (ea_hydro_series, nrw_readings, sepa_values) or how to obtain the sampling_point identifier, so the routing guidance is only implicit.

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

All TerraMCP tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?