dynamical.org Weather & Climate Catalog

List recent forecast runs

list_recent_runs

Read-only

Check run freshness and arrival status for a dynamical.org forecast dataset, from the same public feed status.dynamical.org's dashboard polls.

Only forecast collections are pipeline-monitored today (analysis collections like noaa-gfs-analysis or noaa-mrms-conus-analysis-hourly aren't yet tracked by this feed).

Args: collection_id: A STAC collection id, e.g. "noaa-gfs-forecast". limit: Maximum number of recent runs to return, most recent first (default 10).

Returns: A dict with the overall pipeline sla_status, this product's cadence and next expected init/completion time, typical latency stats, and up to limit recent runs (init_time, status, completion_pct, on_timedness, arrival/latency timestamps). If collection_id isn't pipeline-monitored, returns monitored: False plus the list of collection ids that are.

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": {
      "collection_id": "<collection_id>"
    },
    "name": "list_recent_runs"
  }
}

Input Schema

Name	Required	Description	Default
`limit`	No
`collection_id`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 6, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Annotations already declare readOnlyHint and openWorldHint. The description adds value by detailing the return structure (dict with sla_status, cadence, latency stats, recent runs) and the edge case for non-monitored collections (returns monitored: False). This goes beyond the annotations and provides useful behavioral insights.

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 description is well-structured with a clear header, contextual note, parameter descriptions, and return value shape. Every sentence adds value, though it could be slightly more concise. It is front-loaded with the main purpose.

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?

Given the tool's complexity (2 params, no enums, output schema exists) and the presence of annotations, the description covers purpose, usage constraints, parameter semantics, return value details, and an edge case. It is fully complete for an agent to select and invoke the tool correctly.

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?

With 0% schema description coverage, the description fully compensates. It explains collection_id as a STAC collection id with an example, and limit as max number of recent runs with default and ordering. This adds meaning beyond the input schema's property definitions.

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?

The description clearly states it lists recent forecast runs for a dynamical.org dataset, checking freshness and arrival status. It distinguishes well from sibling tools like get_access_pattern or search_catalog by specifying its unique purpose of monitoring run freshness.

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 explicitly states that only forecast collections are pipeline-monitored, and analysis collections are not tracked. It also describes the behavior when the collection_id is not monitored. While it provides clear context, it doesn't explicitly name alternatives or when not to use it, but the sibling tools provide that differentiation.

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

All dynamical.org Weather & Climate Catalog tools Inspect

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