gribstream_query_timeseries

AUTH REQUIRED / DATA QUERY. Executes an authenticated /api/v2/{dataset}/timeseries request and returns actual weather/forecast data values. Use this tool, not the NO AUTH request-building or metadata tools, when the user asks to fetch, retrieve, download, return, or analyze weather values, CSV/JSON/NDJSON/Parquet data, plots, heatmaps, or best-available values by valid time. Defaults to CSV for compatibility; prefer Parquet for dense grids and large tabular results. DENSE-GRID PLOTS: Send one request.grid and one live query with accept=application/vnd.apache.parquet; do not expand the grid into request.coordinates or spatially tile it first. Save the returned binary resource, sort the decoded rows, and plot that file directly. Spatially tile only after an actual coordinate-limit or response-size error. Return the plot rather than the raw dataset unless the user asks for the data. PARQUET HANDLING: Treat resource.blob as an opaque binary intermediate. Never stringify, print, log, quote, or copy its base64 into assistant-visible text or another tool result. Decode and save it once to suggested_filename in the same execution environment, then read that file with a Parquet-capable library. Do not repeat the same GribStream query merely to materialize or recover the returned file. If the client cannot consume embedded binary resources, use CSV and split the request below max_bytes. For dense grids or large tabular results, request application/vnd.apache.parquet before splitting; Parquet is typed and ZSTD-compressed, and MCP returns it as an embedded binary resource. If Parquet still exceeds the MCP limit, split by time range first, split variables second, and split coordinates/grid/space last; spatial tiling creates the most extra requests and should be the fallback. Do not include request.asOf unless the user explicitly wants backtesting, time travel, or a historical model-run cutoff. Response rows are unsorted; sort by location fields and forecasted_time before plotting, building time series, or checking for gaps. The request body must use request.coordinates and request.variables; do not use location, coord, coords, parameters, parameter, or variable as replacement field names. It is categorised as a Read tool in the GribStream MCP MCP Server, which means it retrieves data without modifying state.

gribstream_query_timeseries accepts 4 parameters: accept, dataset, request, max_bytes. 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_query_timeseries: 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_query_timeseries is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Yes. Add a rate_limit block to the gribstream_query_timeseries 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_query_timeseries. 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_query_timeseries 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.