Hydrata MCP Server

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by Hydrata

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Python

Remote

Poll a run's status

get_run_status

Read-onlyIdempotent

Poll a simulation run's status and get phase, progress, ETA, errors, and compute backend without retrieving full run details.

Instructions

Lightweight status check for a simulation run (fast, <50ms).

Use this for polling instead of get_run. Returns only: id, status, phase, progress_pct (0-100), eta_seconds, error_message, and compute_backend.

phase is never empty: it is status, except preparing_inputs while a computing run has no progress yet (it is downloading its input package and building the domain — minutes on a big model; this is NOT a stall). Poll every 5-10 seconds. Terminal states: complete, error, cancelled.

Input Schema

Name	Required	Description	Default
`run_id`	Yes	The run ID

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.2.1Oct 29, 2026

TDQS

A4.7/5.0

Behavior5/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive), and the description still adds real behavioral context beyond them: sub-50ms latency, the exact projected return fields, phase-vs-status divergence, and the misleading non-stall state. That is genuine disclosure an agent cannot derive from the annotations.

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?

Front-loaded with the fast polling purpose, then the return payload, then the phase nuance, then the cadence. Every sentence carries distinct operational information with no 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?

An output schema exists, yet the description still names the fields an agent will see, and it supplies the polling cadence and terminal states needed to drive a poll loop. Nothing required to call this tool correctly is missing.

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?

The single run_id parameter is already fully described in the schema (100% coverage), so the schema does the work. The description adds no syntax or constraint detail for run_id beyond implying it identifies the run. Baseline 3 is correct for a one-param tool with complete schema coverage.

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?

Opens with a specific verb+resource and scope: 'Lightweight status check for a simulation run (fast, <50ms).' It also names the sibling it replaces ('instead of get_run'), so an agent can distinguish it from get_run 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 Guidelines5/5

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

Explicit routing rule ('Use this for polling instead of get_run') plus operational cadence ('Poll every 5-10 seconds') and terminal-state list (complete, error, cancelled) that tells the agent when to stop. The 'preparing_inputs ... is NOT a stall' note preempts a specific misuse.

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