# io.github.rpconroy/gdex-mcp > MCP server for the GDEX (Geoscience Data Exchange) data portal: datasets, files, metrics, subsetting - name: io.github.rpconroy/gdex-mcp - version: 0.1.0 - connection class: R0 (autonomous) - trust: 90/100 - quality: 76/100 (good) - owner: not claimed - descriptor: https://api.protogrid.dev/v1/servers/io.github.rpconroy%2Fgdex-mcp - tools: https://api.protogrid.dev/v1/servers/io.github.rpconroy%2Fgdex-mcp/tools **An agent can connect right now, no human step.** Remote endpoint, no authentication, reachable on the last probe. ## Connection (mcpServers) ```json { "mcpServers": { "gdex-mcp": { "type": "http", "url": "https://gdex-mcp.k8s.ucar.edu/mcp" } } } ``` ## Trust - hygiene: 100 - liveness: 100 - freshness: 100 - provenance: 65 - drivers: +repository +reachable +uptime-100% +updated-26d-ago - Derived from observable signals (official registry feed and our own credential-free probes); no code audit performed. ## Quality 76/100 (good) ### protocol: 75 (weight 25) - warn `protocol.modern`: newest supported version is 2025-11-25; 2026-07-28 not supported, older versions are deprecated until 2027-07-28 - n/a `protocol.stateless`: only applies to 2026-07-28 servers - pass `protocol.transport`: streamable HTTP - n/a `protocol.list_ttl`: only applies to 2026-07-28 servers ### authorization: n/a (weight 15) - n/a `auth.prm`, `auth.as_metadata`, `auth.cimd`: no remote uses OAuth - n/a `auth.secret_in_url`: no templated URL ### tool hygiene: 64 (weight 30) - pass `tools.descriptions`: every tool has a description - warn `tools.description_length`: 4 tools have descriptions over 1,024 characters, which crowds the context - pass `tools.schemas`: every tool has an object input schema - fail `tools.annotations`: no tool declares readOnlyHint or destructiveHint, so clients cannot tell safe calls from risky ones - fail `tools.directory_hints`: no tool declares all four of readOnlyHint, destructiveHint, idempotentHint and openWorldHint; missing: describe_dataset (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_filesearch_datatypes (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); get_portal_metrics (readOnlyHint, destructiveHint, idempotentHint, openWorldHint); … - pass `tools.token_cost`: about 5,722 tokens to load every tool - pass `tools.api_dump`: tools are not a one-to-one API dump ### stability: 100 (weight 15) - pass `stability.changes`: no tool changes in 30 days - pass `stability.rug_pull`: no tool changed its meaning under the same name ### dependencies: n/a (weight 15) - n/a `deps.known_vulns`, `deps.mcp_sdk_version`, `deps.resolvable`: no npm or PyPI package - tools: 30, about 5,722 tokens to load them all - tool set hash: 3c88a7eda2a751d7cac05c6d77dc8541 - badge: [![protogrid quality](https://protogrid.dev/badge/io.github.rpconroy/gdex-mcp.svg)](https://protogrid.dev/servers/io.github.rpconroy/gdex-mcp) - Checks run on what our credential-free, read-only probes observe; tools are never called and no code audit is performed. ### Tool changes _No tool definition changes recorded._ ## Remotes - https://gdex-mcp.k8s.ucar.edu/mcp (streamable-http, auth none, reachable true, uptime30d 1) ## Packages _none_ ## Tools (30) - `check_request_status`: Check the status of a specific subsetting request. Requires GDEX_TOKEN. Args: rindex: Request index/ID - `describe_dataset`: Return a combined overview of a dataset — abstract, temporal coverage, spatial coverage, variables, data formats, and volume — in a single call. Prefer this over calling get_dataset_field repeatedly when the user wants a general summary of a dataset. If one of the underlying fields fails to load, it's returned as {"error": ...} rather than failing the whole call. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 - `find_dataset_files`: Search a dataset's file-group hierarchy for files matching a name pattern (e.g. a date like "20220808"), without ever pulling a large, context-blowing file listing. Automates the pattern described in get_file_groups: recursively calls get_file_groups, descending into every child gindex, until a gindex returns no further children (a leaf group) — then calls get_dataset_files there with filter_wfile=name_pattern and keeps only the matches. Prefer this over manually drilling with get_file_groups/get_dataset_files when you don't already know roughly where in the hierarchy to look. A dataset's hierarchy can be large (hundreds of leaf groups), and this tool has no way to know in advance which branches might contain a match, so it may need to visit many groups to be thorough. Pass start_gindex if you already know a good starting point (e.g. from a prior get_file_groups call, or a related dataset's structure) to narrow and speed up the search. If the number of groups visited hits max_groups_visited, the search stops early and `stopped_early` comes back true — narrow with start_gindex and retry, or raise the cap. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 name_pattern: Filename substring/pattern to match, e.g. "20220808" for a date start_gindex: Optional group index to start the search from, instead of the dataset root max_groups_visited: Safety cap on groups traversed before giving up (default 300) - `get_arco_variables`: Return the list of ARCO variables available for a dataset. Each row's type (index 2) is either "zarr" or "reference" (kerchunk). A "reference" variable is often listed multiple times, once per kerchunk reference variant (plain, "-remote-https", "-remote-osdf", etc.) — always pick the "-osdf" variant's URL; the others' chunk targets can be internal paths that only resolve on NCAR's network. Open it with xr.open_dataset(url, engine="kerchunk", storage_options={"remote_protocol": "https", "lazy": True}) — that's better behaved than hand-building an fsspec reference filesystem. A "zarr" row needs no variant picking — its URL already routes through the OSDF director (rewritten automatically); open it directly with xr.open_dataset(url, engine="zarr"). Results are capped at 300 rows (look for "truncated": true); narrow with search_arco_variables if you hit the cap. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 - `get_control_file_template`: Return the control file template for building a subsetting request for a dataset. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 - `get_data_access`: Return data access options for a dataset — download links, Globus URLs, access methods. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 - `get_dataset_field`: Return one metadata field for a dataset. Prefer describe_dataset when the user wants a general summary covering several of these at once (abstract, temporal, spatial_coverage, variables, data_formats, volume) — this tool is for pulling a single field, including the fields describe_dataset doesn't cover (publications, contributors, related_datasets, documentation). Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 field: One of: abstract, variables, temporal, spatial_coverage, publications, contributors, data_formats, volume, related_datasets, documentation - `get_dataset_files`: Return a paginated file listing for a dataset or a specific group. Depending on how deep gindex is in the group hierarchy, this returns either actual file rows or another layer of subgroup summaries — there's no way to tell in advance which you'll get. If you get subgroups, read the gindex (or url) off a row and call again with that gindex to go one level deeper; gindex values are dataset-specific and can't be guessed. A shallow gindex on a large dataset can return a very large response (thousands of files) — the file-row portion of the response is capped at 500 rows (look for "_truncated": true). Two ways to avoid hitting that cap instead of drilling down group by group: pass filter_wfile with a filename pattern (e.g. a date like "20220808") to filter down to matching files, or page through a known group's results with `page`. filter_wfile only filters actual file rows, so it has no effect at a gindex that's still returning a subgroup summary rather than files — if a first attempt comes back unfiltered, descend one level (see get_file_groups) and retry there. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 gindex: Optional group index to filter files page: Page number to fetch (for a group with more files than fit on one page) filter_wfile: Filter files by name pattern, e.g. "20220808" to match a date fl: File list source (defaults to "web" server-side) - `get_dataset_metadata`: Return full metadata for a GDEX dataset (parameters, temporal range, spatial coverage, etc.). Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 - `get_dataset_metrics`: Return a per-dataset metric. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 metric: One of: users_month, users_year, volume_month, volume_year - `get_file_groups`: Return file groups for a dataset. Pass gindex to get child groups under a parent. Groups nest (dataset -> format -> year -> month, or similar, varying by dataset) and there's no way to predict a child's gindex in advance — each group's gindex/url is dataset-specific and only knowable from the parent response. To drill down, read the gindex (or url) off a row in this response and pass that as the next call's gindex. This never returns file rows, only groups — every response stays small regardless of how many files the dataset holds, unlike get_dataset_files. Descend until a call returns empty ({} or []): that means the gindex you just called with is a leaf with no further subgroups, so it's safe to call get_dataset_files there for the actual files. find_dataset_files automates exactly this walk if you'd rather not do it by hand. At the top level, watch for a "Kerchunk Reference Files" (or similar ARCO-related) group alongside the raw-format groups. For an analysis task, prefer pulling from there (see also has_arco/get_arco_variables) over a raw data file when one's available — it avoids downloading a whole file just to read a subset of it. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 gindex: Optional group index to fetch child groups - `get_filesearch_cyclone_fix_files`: Search for data files containing "cyclone_fix" datatype data, optionally filtered by time range. Results are paginated; use get_filesearch_result_page with the returned result_id to fetch additional pages. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 valid_datetime_min: Restrict to data valid on/after "YYYY-MM-DD HH:MM" valid_datetime_max: Restrict to data valid on/before "YYYY-MM-DD HH:MM" - `get_filesearch_cyclone_fix_filters`: Return the valid date range and other filters for "cyclone_fix" datatype file search on a dataset. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 valid_datetime_min: Restrict to data valid on/after "YYYY-MM-DD HH:MM" valid_datetime_max: Restrict to data valid on/before "YYYY-MM-DD HH:MM" - `get_filesearch_datatypes`: Return the file-search datatypes available for a dataset (a subset of "grid", "cyclone_fix", "sensor"). Call this before the other filesearch_* tools to know which one(s) apply — a dataset only supports search for the datatypes it actually contains. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 - `get_filesearch_grid_files`: Search for data files containing "grid" datatype data, filtered by parameter code(s) and optionally by time range, product, grid, or level. Results are paginated; use get_filesearch_result_page with the returned result_id to fetch additional pages. Use get_filesearch_grid_filters first to find valid parameter/product/grid/level codes for this dataset. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 parameters: Parameter code(s) to search for (required, at least one) valid_datetime_min: Restrict to data valid on/after "YYYY-MM-DD HH:MM" valid_datetime_max: Restrict to data valid on/before "YYYY-MM-DD HH:MM" products: Restrict to specified product code(s) grids: Restrict to specified grid code(s) levels: Restrict to specified vertical level code(s) - `get_filesearch_grid_filters`: Return the valid parameter/product/grid/level codes and date range for "grid" datatype file search on a dataset. Use this to discover the codes to pass to get_filesearch_grid_files, optionally narrowed by any filters you already know you want. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 valid_datetime_min: Restrict to data valid on/after "YYYY-MM-DD HH:MM" valid_datetime_max: Restrict to data valid on/before "YYYY-MM-DD HH:MM" parameters: Restrict to specified parameter code(s) products: Restrict to specified product code(s) grids: Restrict to specified grid code(s) levels: Restrict to specified vertical level code(s) - `get_filesearch_result_page`: Return a page of results from a previous get_filesearch_*_files call, by its result_id. Use this to page through file-search results beyond the first page (see the "pagination" block of a files/results response for num_pages and next_page). Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 result_id: The result_id from a previous filesearch files/results response page_num: Page number to retrieve - `get_filesearch_sensor_files`: Search for data files containing "sensor" datatype data, optionally filtered by date range. Results are paginated; use get_filesearch_result_page with the returned result_id to fetch additional pages. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 valid_date_min: Restrict to data valid on/after "YYYY-MM-DD" valid_date_max: Restrict to data valid on/before "YYYY-MM-DD" - `get_filesearch_sensor_filters`: Return the valid date range and other filters for "sensor" datatype file search on a dataset. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 valid_date_min: Restrict to data valid on/after "YYYY-MM-DD" valid_date_max: Restrict to data valid on/before "YYYY-MM-DD" - `get_portal_metrics`: Return a GDEX portal-wide metric. Args: metric: One of: volume_downloaded, unique_users, total_datasets, total_citations, gdex_volume, total_requests, top_datasets, ai_datasets - `get_request_files`: Return the output files for a completed subsetting request. Requires GDEX_TOKEN. Args: rindex: Request index/ID - `get_staff`: Return GDEX staff contacts, optionally filtered to a specific dataset. Args: dsid: Optional dataset ID. If omitted, returns all staff. - `has_arco`: Check whether Analysis-Ready Cloud-Optimized (ARCO) data is available for a dataset. For analysis tasks, call this (and check for a "Kerchunk Reference Files" group via get_file_groups) before reaching for raw data files — reading through ARCO/kerchunk references avoids downloading whole files just to subset them. When picking among kerchunk reference variants, always use the one with "-osdf" in its name (see get_arco_variables) — other variants' chunk targets can be internal paths that only resolve on NCAR's network. Open it with xr.open_dataset(url, engine="kerchunk", storage_options={"remote_protocol": "https", "lazy": True}) rather than hand-building an fsspec reference filesystem. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 - `list_datasets`: List datasets available on GDEX, with their IDs and titles. The full catalog has ~1700 datasets — far too many to return at once. Always pass `query` to filter by keyword unless the user specifically wants to browse the whole catalog page by page. Args: query: Keyword(s) to filter by, matched case-insensitively as a substring against dataset id and title. Leave empty to browse unfiltered. limit: Max number of datasets to return (default 50, capped at 500) offset: Number of matching datasets to skip, for paging through results - `list_request_statuses`: List all subsetting request statuses for the authenticated user. Requires GDEX_TOKEN. - `purge_request`: Delete a subsetting request and its output files. Requires GDEX_TOKEN. Args: rindex: Request index/ID to purge - `search_arco_variables`: Search ARCO variables by name for a dataset. As with get_arco_variables, a "reference" (kerchunk) match is often listed once per variant — always pick the "-osdf" variant's URL (reachable from anywhere), and open it with xr.open_dataset(url, engine="kerchunk", storage_options={"remote_protocol": "https", "lazy": True}). A "zarr" match needs no variant picking — its URL already routes through the OSDF director (rewritten automatically); open it directly with xr.open_dataset(url, engine="zarr"). Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 query: Search text to match against variable names - `submit_and_wait_for_request`: Submit a subset request and poll its status until it finishes, fails, or timeout_s elapses — instead of calling submit_subset_request and then manually looping on check_request_status. Requires GDEX_TOKEN. GDEX's API schema doesn't document the exact status vocabulary, so "finished" is a best-effort match on the status text (words like "complete" vs. "error"/"fail"). If the outcome comes back "timeout", that means the request is still pending by our reading, not that it failed — keep polling with check_request_status(rindex), or re-run this tool with a longer timeout_s. If it comes back "unknown", the status payload didn't contain a field we recognize; inspect the raw "status" value yourself. Args: request_json: JSON string of the subsetting request body (see get_control_file_template). poll_interval_s: Seconds between status checks (default 15, minimum 5). timeout_s: Give up and return the last-seen status after this many seconds (default 600). - `submit_subset_request`: Submit a data subset request to GDEX. Requires GDEX_TOKEN. Args: request_json: JSON string of the subsetting request body. Use get_control_file_template to get the expected structure for a dataset, or validate_subset_request to check it before submitting. - `validate_subset_request`: Check a subset request body against the dataset's control file template, without submitting anything. Use this before submit_subset_request to catch missing or unrecognized fields fast, instead of finding out from a failed API call. Args: dsid: Dataset ID (dNNNNNN), e.g. d083002 request_json: JSON string of the subsetting request body to validate