SPEDAS Agent Kit

spedas_agent_kit is the SPEDAS organization MCP server for agentic heliophysics workflows. It presents one SPEDAS-facing data layer and organizes capabilities by data source category instead of by the internal backend packages used to implement them.

The current design follows Jason's A+B direction:

• A. SPEDAS data layer — one unified entry point for source categories such as cdaweb, pds, and spice/geometry.

• B. SPEDAS science workflow layer — high-level planning tools that let Claude Code, Codex, OpenCode, LingTai, or another agent start from a science question before choosing source-specific operations.

Implementation backend packages should stay visible to maintainers, but they should not be the user's first mental model.

Repository

• Official repo: https://github.com/spedas/spedas_agent_kit

• Python package name: spedas-agent-kit

• Python module / CLI module: spedas_agent_kit

• Canonical shared skills: src/spedas_agent_kit/resources/skills/ (packaged with the kit; wrappers should stay thin)

• Default MCP tool count: 13 (legacy CDAWeb/PDS compatibility tools are conditionally registered); advertised tools carry MCP ToolAnnotations plus meta.surface (primary or compat) so launchers can filter by surface and side-effect hints.

Related MCP server: space-mcp-server

Practical guide: run a SPEDAS Agent Kit study

Use this section as the README-level operating guide for researchers and agents. The detailed capability map below is the reference; this guide is the shortest safe path from a science question to reproducible artifacts.

The default loop

1. Restate the science question and constraints. Capture the target, mission, instrument or observable, time range, and whether the request is heliophysics, planetary, geometry-only, or analysis oriented.

2. Ask SPEDAS Agent Kit to choose the data-source family before fetching data. Start with spedas_overview(), then call search_spedas_data_sources(...) or plan_spedas_observation(...). Do not jump directly to a low-level archive tool just because a mission name matched a backend.

3. Create a run directory. For work that may fetch, transform, render, or be cited later, call create_spedas_analysis_bundle(...) first and keep data, plots, provenance, and notes under that bundle.

4. Browse narrowly, then fetch narrowly. Use the data-layer tools to browse source categories and parameters. Keep public-archive requests to small, reproducible intervals and explicit parameters.

5. Use geometry as a follow-on step. SPICE geometry, ephemeris, and frame transforms consume explicit files/artifacts. They should not hide large downloads or return bulk arrays inline.

6. Return paths, provenance, and caveats. A good answer names the source, dataset/product, variables, time window, output files, validation/caveats, and the next reproducible command. It does not paste CDF contents or giant arrays into chat.

Choose the leading source family

Minimal MCP call sequence

For an open-ended question, the safe skeleton is:

spedas_overview()
search_spedas_data_sources(question="...", target="...", observables=[...])
plan_spedas_observation(science_goal="...", start="...", stop="...", target="...", observables=[...])
create_spedas_analysis_bundle(study_name="...", output_dir="...")
browse_data_sources(source_type="cdaweb|pds|spice")
load_data_source(source_type="...", source_id="...")
browse_data_parameters(source_type="...", dataset_id="...")
fetch_data_product(source_type="...", dataset_id="...", parameters=[...], start="...", stop="...", output_dir="...")

Add geometry only when the plan calls for it:

get_ephemeris(...)
compute_distance(...)
transform_coordinates(...)

Practical recipes

• PSP perihelion solar wind: route the science question first, let CDAWeb lead measurement discovery, then add SPICE only for spacecraft-Sun geometry. See docs/examples/psp_perihelion_solar_wind.md.

• MMS magnetopause interval: use plan_spedas_observation to keep mission, observable, and interval explicit; fetch selected CDAWeb variables into an analysis bundle before plotting or transforming. See docs/examples/mms_magnetopause_workflow.md.

• Juno / planetary plasma interactions: let PDS lead MAG/plasma archive discovery and use SPICE as a geometry companion. See docs/examples/juno_pds_spice_workflow.md.

• Overview + geomagnetic-index context: for IDL-SPEDAS-style summary plots or Dst/AE/Kp/SYM-H context, load the Claude skill overview-geomagnetic-indices and use spedas_overview()["guided_recipes"] to map the intent to CDAWeb/HAPI OMNI datasets or PySPEDAS Kyoto/NOAA loaders.

Artifact and provenance contract

Every non-trivial run should leave a directory that another researcher can audit:

<run>/
  requests/      original prompt, plan, or recipe
  data/          fetched or prepared measurement files
  plots/         PNG/SVG/PDF renderings
  provenance/    source IDs, parameters, cache notes, tool versions, hashes
  notes/         interpretation, caveats, and next steps

When reporting results, include at least:

• science goal and time range;

• selected source family (cdaweb, pds, or spice);

• dataset/product IDs and parameters/variables;

• output files and hashes when available;

• dependency or data-access caveats (missing_dependency, archive rate limits, cache-only validation, unavailable kernels, no matching station, etc.);

• the next command or MCP call needed to reproduce or extend the run.

Agent safety checklist

• Prefer the unified data-layer and science-workflow tools over compatibility low-level tools for new work.

• Do not infer a source from one keyword. Use target + mission + observable + time context, especially for planetary versus near-Earth uses of generic words such as "magnetosphere", "bow shock", "radiation belt", "solar wind", and "energetic particle".

• Keep fetches narrow. Public archives can rate-limit or be cold; long intervals should be split deliberately and recorded in provenance.

• The base install exposes the bundled CDAWeb/PDS/SPICE surface only; the optional analysis, hapi, and fdsn extras (and their tools) were removed in the one-MCP cleanup.

• Validate generated artifacts before interpreting them. Check file existence, row/sample counts, time coverage, coordinate frame, and whether the tool returned warnings or caveats.

Layered capability map

1. Data layer tools

Start here when the user asks for data, datasets, parameters, products, archives, or cache status.

• browse_data_sources(source_type="all", query=None) — browse SPEDAS data source categories, or drill into one category.

• load_data_source(source_type, source_id, mode="compact", limit=None, offset=0, instrument=None, dataset_query=None, include_full_prompt=False) — load source context. CDAWeb observatories default to a compact structured dataset page (dataset IDs, instruments, coverage, next calls); use limit/offset and filters for large catalogs, or mode="full" / include_full_prompt=True for the legacy full prompt.

• browse_data_parameters(source_type, dataset_id, dataset_ids=None) — browse parameters/metadata for CDAWeb or PDS datasets; for SPICE, returns geometry/frame context.

• fetch_data_product(source_type, dataset_id, parameters, start=None, stop=None, output_dir=None, format="csv", limit=None) — unified measurement/archive data fetch for CDAWeb/PDS. SPICE requests are routed to geometry tools instead. limit is currently a CDAWeb-oriented safety control; PDS fetches should be narrowed by time/parameters.

• manage_data_cache(source_type="all", action="status", cache_dir=None, mission=None, ...) — unified cache status/maintenance for the source categories. It passes source-specific cache options through one advertised tool: CDAWeb (category, observatory, dataset_ids, older_than_days, dry_run, detail), PDS (category, mission, dataset_ids, older_than_days, dry_run, detail, force), and SPICE (mission, filenames). Per-call cache_dir is reported as guidance only; backend cache roots are configured by the MCP server environment.

Supported source_type values:

Compact CDAWeb catalog discovery examples:

# Default MMS page is compact (<12 KB) and includes exact next calls per dataset.
load_data_source(source_type="cdaweb", source_id="mms")

# Page through or narrow large observatories.
load_data_source(source_type="cdaweb", source_id="mms", limit=10, offset=10)
load_data_source(source_type="cdaweb", source_id="mms", instrument="fgm", dataset_query="srvy")

# Opt into the legacy human prompt only when needed.
load_data_source(source_type="cdaweb", source_id="mms", mode="full")

2. Science workflow tools

Start here for open-ended science requests.

• spedas_overview() — compact map of capability groups and recommended workflow.

• search_spedas_data_sources(question, target=None, observables=None) — recommend which data source categories should lead a request.

• plan_spedas_observation(science_goal, start=None, stop=None, target=None, observables=None, data_sources=None) — produce a source-specific plan before fetching data.

• compare_cdaweb_pds_spice(science_goal="") — explain source boundaries and choose the right source family.

• create_spedas_analysis_bundle(study_name, output_dir, ...) — create a request/provenance scaffold with requests/, data/, plots/, provenance/, and notes/ folders.

3. Geometry tools

SPICE is exposed as a data source category, but geometry operations are clearer as explicit tools:

• Browse SPICE missions with browse_data_sources(source_type="spice"); the response also includes a frame_catalog with frame descriptions and aliases.

• Browse the same SPICE coordinate-frame catalog explicitly with load_data_source(source_type="spice", source_id="frames") or browse_data_parameters(source_type="spice", dataset_id="frames"); use supported_frame_names as transform_coordinates from_frame/to_frame values.

• get_ephemeris(mission, target, start, stop, step="1h", frame="J2000", observer=None)

• compute_distance(mission, target, observer, start, stop, step="1h")

• transform_coordinates(mission, coordinates, from_frame, to_frame, epoch=None)

SPICE kernel cache status/load/clean/check/purge actions are exposed through manage_data_cache(source_type="spice", action=..., mission=..., filenames=...).

4. Compatibility low-level tools

These remain available for clients that already know the source-specific browse/fetch operations:

• CDAWeb: browse_observatories, load_observatory, browse_parameters, fetch_data

• PDS: browse_pds_missions, load_pds_mission, browse_pds_parameters, fetch_pds_data

• SPICE: the geometry tools above

The former dedicated cache tools (manage_cdaweb_cache, manage_pds_cache, manage_spice_kernels) are no longer advertised as MCP tools because their actions and kwargs are covered by manage_data_cache. See docs/public_api_strategy.md for the compatibility map and deprecation guidance.

PDS PPI archive tree map

data/pds_archive_map.json is a crawled map of the PDS Planetary Plasma Interactions (PPI) node's public archive tree (https://pds-ppi.igpp.ucla.edu/data, an Apache autoindex-style listing). It is produced by scripts/crawl_pds_archive.py and refreshed the same way:

python scripts/crawl_pds_archive.py --limit 2000 --max-depth 8 --workers 6
python scripts/crawl_pds_archive.py --resume            # continue a partial crawl
python scripts/crawl_pds_archive.py --consolidate-only  # rebuild asset from JSONL sidecar

Each directory node records its relative path, URL, depth, child directory names, file count, min/max child mtime, aggregate file size (when the listing exposes it), and a is_leaf flag marking folders that contain data files and no subdirectories. Crawls are bounded by --limit (directory listings), --max-depth, and an optional --max-time; per-node HTTP errors are recorded and never abort the crawl. The committed asset is a bounded sample (2005 directory nodes, 840 leaf data folders as of the crawl); nodes whose children were not expanded are flagged with children_explored: false.

NAIF SPICE kernel archive tree map

data/spice_archive_map.json is a crawled map of the NAIF SPICE kernel archive tree (https://naif.jpl.nasa.gov/pub/naif/, an Apache autoindex-style listing). It is produced by scripts/crawl_spice_archive.py (modeled on the PDS crawler, plus an ftplib FTP fallback for directories HTTP cannot serve) and refreshed the same way:

python scripts/crawl_spice_archive.py --limit 2000 --max-depth 8 --workers 6
python scripts/crawl_spice_archive.py --resume            # continue a partial crawl
python scripts/crawl_spice_archive.py --consolidate-only  # rebuild asset from JSONL sidecar

Each directory node records its relative path, URL, depth, child directory names, file count, min/max child mtime, aggregate file size (when the listing exposes it), the SPICE kernel file extensions present, and two classification flags: is_leaf (files, no subdirectories) and is_kernel_leaf (a leaf folder containing at least one kernel file such as .bsp/.bpc/.bc/.tf/.tls/.tsc/.tpc/.ti/.tl/.tm/.mk/.txt). Crawls are bounded by --limit (directory listings), --max-depth, and an optional --max-time; per-node HTTP errors are recorded and never abort the crawl, and --resume re-enqueues the children of parents whose exploration was cut short. The committed asset is a bounded sample (2005 directory nodes, 864 kernel leaf folders as of the crawl — the full NAIF tree is much larger); nodes whose children were not expanded are flagged with children_explored: false and the asset carries "partial": true.

Recommended agent workflow

1. Call spedas_overview().

2. For a natural-language science request, call search_spedas_data_sources(...) or plan_spedas_observation(...).

3. Use the data layer:

   • browse_data_sources(source_type="all")

   • browse_data_sources(source_type="cdaweb" | "pds" | "spice")

   • load_data_source(...)

   • browse_data_parameters(...)

   • fetch_data_product(...) for CDAWeb/PDS measurement/archive products

4. Use geometry tools directly for SPICE ephemeris, distance, frame, and coordinate-transform work.

5. For any real analysis, call create_spedas_analysis_bundle(...) and write fetched files under the generated data/ directory.

6. Return compact summaries and file paths. Do not paste large science arrays into chat.

Quick start for local development

git clone https://github.com/spedas/spedas_agent_kit.git
cd spedas_agent_kit
uv sync --extra dev --extra mcp
uv run --extra mcp python -m spedas_agent_kit

Run tests and smoke checks:

uv run --extra dev --extra mcp python -m pytest -q
uv run --extra mcp python scripts/smoke_mcp_list_tools.py --json

The list-tools smoke starts the stdio MCP server with isolated temporary cache directories, performs MCP initialize + list_tools, and verifies the expected advertised tool names. It does not fetch CDAWeb/PDS data or download SPICE kernels.

The base install is sufficient for the full CDAWeb/PDS/SPICE tool surface; the optional analysis, hapi, and fdsn extras (and their tools) were removed in the one-MCP cleanup.

MCP client configuration

Example stdio configuration:

{
  "mcpServers": {
    "spedas": {
      "command": "uv",
      "args": ["run", "--extra", "mcp", "python", "-m", "spedas_agent_kit"],
      "cwd": "/path/to/spedas_agent_kit"
    }
  }
}

For plugin-style distribution, the canonical standalone wrappers now live in separate SPEDAS org repos:

• https://github.com/spedas/spedas_claude — Claude Code plugin wrapper.

• https://github.com/spedas/spedas_codex — Codex plugin wrapper.

The in-repo plugin fixtures were removed in the one-MCP cleanup; runtime-specific packaging should evolve in the standalone repos while this repository owns the MCP server itself. The current base list_tools count is 13, plus 8 legacy CDAWeb/PDS compatibility tools when SPEDAS_AGENT_KIT_COMPAT_TOOLS=1.

Maintainer-facing positioning

spedas_agent_kit should be thick at the SPEDAS data/workflow layer and thin at the backend implementation layer:

• Users see one SPEDAS Agent Kit and one data layer.

• Data source categories are scientific concepts: CDAWeb, PDS, SPICE/geometry.

• Backend packages remain maintainable internal implementation surfaces.

• Higher-level tools should encode reusable SPEDAS scientific method: source selection, planning, provenance, and artifact discipline.

See docs/maintainer_note.md and docs/examples/agent_workflow.md for the current framing.

• docs/examples/juno_pds_spice_workflow.md — Juno MAG/PDS discovery plus SPICE geometry planning, including current caveats.

Periodic catalog drift scans

The vendored data catalogs (CDAWeb observatory datasets, PDS PPI archive slots, SPICE kernel manifests) are checked against the live upstream archives by scripts/scan_drift.py — a stdlib-only scanner (plus requests) that maintains a heuristic drift manifest per data-folder layer:

Each scan records every dataset's HTTP status (200/404/redirect), the actual upstream master filename/casing, and classifies catalog rot as renamed | case_change | 404 | param_change | date_anomaly. Results are written incrementally to drift_scan_results.jsonl (resumable on interrupted runs) and rendered to a committed DRIFT.md snapshot with totals, the drifted-dataset table, and a new/recovered/persistent scan-history note vs the previous manifest. Run it as a periodic (e.g. weekly) smoke scan — full sweeps of all 2917 CDAWeb datasets / 1646 SPICE kernels are slow:

# quick smoke runs (recommended for CI or a cron)
python scripts/scan_drift.py --layer cdaweb --limit 50
python scripts/scan_drift.py --layer pds --limit 50
python scripts/scan_drift.py --layer spice --limit 20

# full sweep (slow; needs network to CDAWeb / PDS PPI / NAIF)
python scripts/scan_drift.py --layer cdaweb

--seed-dir DIR (default: $SPEDAS_DRIFT_SEED_DIR, else the temp dir if prior audit artifacts are present) lets a fresh checkout be seeded best-effort from previous audit results; pass --no-seed to skip seeding. The initial manifests were seeded from the 2026-08-03 backend audit artifacts.

A full scheduled scan is intentionally not wired into CI: it needs live network access to the upstream archives and would be flaky/slow in CI. Only the documented smoke commands above (or a maintainer-run cron job) should invoke it.