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Hydrata MCP Server

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by Hydrata

List a project's time series

list_time_series
Read-onlyIdempotent

Lists a project's time series—rain gauges, hydrographs, tides—with metadata like units, timezone, and row counts, excluding data rows. Use it to discover series IDs before fetching values via the REST API.

Instructions

List a project's time series (rain gauges, hydrographs, tides) — never their rows.

Each series: id, name (a rainfall polygon binds to its gauge by this exact name), series_type, units, timezone, authored_method (how an agent made it; null when nobody said), row_count, first_timestamp and last_timestamp. The data rows themselves are dropped (a project's list is megabytes); fetch one series with the REST API (GET /projects//time-series//) if you need its values.

Input Schema

NameRequiredDescriptionDefault
project_idYesThe project ID

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare read-only, idempotent, non-destructive; the description goes beyond them by disclosing that row data is dropped and the reason (a project's list is megabytes), which is exactly the behavioral context annotations can't express. It omits pagination/ordering behavior, so not a 5.

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-loaded with the verb, scope, and the row exclusion before the field list; no filler sentences. The field enumeration partly duplicates the existing output schema, which costs it a point, though the semantic notes attached to fields keep it earning its space.

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

Completeness4/5

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

For a one-parameter read tool with an output schema, the definition is essentially complete — what it returns, what it omits, and where to go for values. Only missing element is ordering/pagination expectations for a potentially large list.

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% for the single project_id parameter, so baseline is 3. The description's 'a project's time series' reinforces scoping but adds no format, ID-source, or validation detail beyond the schema.

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+resource ('List a project's time series') and immediately bounds scope with '— never their rows', heading off the most likely misuse. The parenthetical examples (rain gauges, hydrographs, tides) make the resource concrete, and it is clearly separable from create/update/delete_time_series siblings.

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?

Explicitly says when not to expect values and names an alternative path ('fetch one series with the REST API (GET /projects/<id>/time-series/<id>/)'). It does not distinguish itself from nearby siblings such as get_project or list_inputs, so the routing advice is clear but not exhaustive.

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