gribstream_build_runs_request
NO AUTH / PUBLIC / READ-ONLY. Builds and validates a copy-pasteable authenticated /api/v2/{dataset}/runs HTTP request without sending it. This tool does not execute the request, query weather values, or return forecast data. Use gribstream_query_runs when the user asks for actual model-run foreca...
This record as markdown: /tools/com-gribstream-mcp/gribstream-build-runs-request.md
What gribstream_build_runs_request does on GribStream MCP
AI agents invoke gribstream_build_runs_request to trigger actions in GribStream MCP. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
accept | string | — | Desired response format for the eventual HTTP request. Prefer application/vnd.apache.parquet for dense grids and large typed tabular results. |
dataset | string | Yes | Dataset code, for example gfs, ifsoper, nbm, hrrr, or urma. |
request | object | Yes | Runs request body. Use either timesList or forecastedFrom+forecastedUntil as UTC model run times. Coordinates must be in request.coordinates, and variable selec |
Parameters from the server's own tool schema.
Why gribstream_build_runs_request is rated High
gribstream_build_runs_request triggers real processes with real consequences. An agent gone sideways doesn't fire it once. It starts dozens of builds, sends mass notifications, or burns through compute before anyone looks up.
Risk signalsAccepts freeform code/query input (request.expressions[].expression) · High parameter count (37 properties)
Attacks that exploit this kind of access
The rule that runs gribstream_build_runs_request safely
PolicyLayer is an MCP gateway: it sits between your AI agents and GribStream MCP, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For gribstream_build_runs_request, this is the rule to start with:
gribstream_build_runs_request stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect GribStream MCP, apply this rule, and every gribstream_build_runs_request call is checked against it from then on.
Questions about gribstream_build_runs_request
NO AUTH / PUBLIC / READ-ONLY. Builds and validates a copy-pasteable authenticated /api/v2/{dataset}/runs HTTP request without sending it. This tool does not execute the request, query weather values, or return forecast data. Use gribstream_query_runs when the user asks for actual model-run forecast data or CSV/JSON/NDJSON/Parquet data. Generated direct API requests include Accept-Encoding: gzip, and generated curl commands use --compressed so large responses can be transferred compressed when the client supports it. The request body must use exact selectors discovered from the catalog or shared-parameter tools, with coordinates in request.coordinates and selectors in request.variables. It is categorised as a Execute tool in the GribStream MCP MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
gribstream_build_runs_request accepts 3 parameters: accept, dataset, request. Required: dataset, request. The full parameter table on this page comes from the server's own tool schema.
Register the GribStream MCP server in PolicyLayer and add a rule for gribstream_build_runs_request: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches GribStream MCP. Nothing to install.
gribstream_build_runs_request is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the gribstream_build_runs_request rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for gribstream_build_runs_request. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
gribstream_build_runs_request is provided by the GribStream MCP server (https://gribstream.com/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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