ard-kit

Self-hosted Agentic Resource Discovery (ARD) for local and private resources: catalog your own scripts/tools into an ai-catalog.json, then serve it through the two surfaces the spec defines — a static manifest at /.well-known/ard.json and a dynamic POST /search endpoint. python3 selfcheck.py asserts the pipeline end-to-end, and .gitlab-ci.yml runs it plus the official conformance CLI (ards-project/ard-spec) on every push where runner quota allows.

ARD is the discovery layer that sits in front of MCP (tools), Skills (instructions), and A2A (agents). It answers one question: "what capability can help with this task?" — invocation stays with the resource's own mechanism.

Why this exists

The pieces started life as a private in-house integration — a catalogizer over a large pile of local scripts and a registry serving them for intent-based lookup. When the ARD specification was announced (HF blog: Agentic Resource Discovery: Let agents search), the salvageable pieces were pulled out, generalized, and aligned to the spec. The original registry server code was lost in a workspace cleanup; registry.py here is a faithful rebuild against the public shape.

Pieces

No dependencies beyond Python 3.10+ stdlib.

Install

pipx install agentic-ard-kit  # isolated venv, three commands on PATH
pipx install .         # same, from a checkout

This installs ard-catalogize, ard-registry and ard-mcp — the same entry points as python3 catalogize.py, python3 registry.py and python3 mcp_server.py. Running straight from a clone stays supported and needs no install at all.

Qoder plugin

ard-kit also ships as a self-contained Qoder plugin — the same code plus an agent-facing wrapper:

qoder plugins install ./ard-kit   # then /plugins reload
qoder plugins validate ./ard-kit  # manifest + component check

Quickstart

# 1. Catalog your scripts and agent skills (default scan dir: ./scripts)
python3 catalogize.py --dir scripts --dir .agents/skills --host myhost.example.com

# 2. Serve it
python3 registry.py --catalog ai-catalog.json --port 8390

# 3. Search (the ARD registry API shape)
curl -s http://127.0.0.1:8390/search \
  -H 'Content-Type: application/json' \
  -d '{"query": {"text": "scan subdomains"}, "pageSize": 5}'

# 4. Inspect before running (invoke command + CLI arguments)
curl -s 'http://127.0.0.1:8390/inspect?identifier=urn:air:myhost.example.com:script:scripts:scan_subdomains'

# 5. Static manifest (for crawlers / federation)
curl -s http://127.0.0.1:8390/.well-known/ard.json

The discover → inspect → run flow mirrors commercial directories like monid.ai, minus the marketplace: POST /search finds the capability, GET /inspect?identifier=<urn> returns its invoke command and parsed argparse flags (404 for unknown identifiers, 501 when the sidecar is absent), and the run itself stays with your shell.

For clients: the registry is stateless and nothing attaches to your session. POST /search returns ranked summaries only; pull a full invoke schema via /inspect for the single entry you actually run. Discard both afterwards — keep the URN if you might reuse the tool, not the payload. Discovery costs a query, not a mounting: there is nothing to unload because nothing was loaded.

Require a bearer token on every endpoint except /health when the registry leaves loopback. For service deployments prefer the ARD_REGISTRY_TOKEN environment variable over --token — the command line is world-readable:

python3 registry.py --catalog ai-catalog.json --port 8390 --token "$(openssl rand -hex 16)"
# or: ARD_REGISTRY_TOKEN=<token> python3 registry.py --catalog ai-catalog.json --port 8390

A token that is set but empty is rejected at startup (fail closed).

Federate with peer registries — with "federation": "auto" (default) queries fan out to --upstreams and results merge (every result names its source registry and carries a clamped score); "federation": "referrals" returns the peers in a referrals array instead; "federation": "none" stays local:

python3 registry.py --catalog ai-catalog.json --port 8390 \
  --upstreams https://peer.example.com --public-url https://me.example.com

Self-check the whole pipeline:

python3 selfcheck.py

MCP front

The registry is HTTP, which means something has to hand the agent a URL first. Editors that speak MCP mount a command instead, so the same catalog is also served over stdio — no server to start, no port, no token:

{
  "mcpServers": {
    "ard": {
      "command": "python3",
      "args": ["/path/to/ard-kit/mcp_server.py",
               "--catalog", "/path/to/ai-catalog.json"]
    }
  }
}

Installed through pipx, the same front is "command": "ard-mcp" with no path to keep in sync.

Two tools, the same discover → inspect → run contract:

Search deliberately returns summaries only: the invoke schema arrives once, for the single entry the agent actually runs. The catalog is re-read per call, so re-running catalogize.py is picked up without a restart, and the host owns the process lifetime — the server exits when stdin closes.

Entry shape

Each catalog entry carries the fields ARD consumers key on:

{
  "@context": "https://agenticresourcediscovery.org/context/v1",
  "identifier": "urn:air:myhost.example.com:script:scripts:scan_subdomains",
  "displayName": "scan_subdomains",
  "description": "Enumerate subdomains via passive sources",
  "type": "application/vnd.ard-kit.script+json",
  "url": "file:///path/to/scripts/scan_subdomains.py",
  "tags": ["scan", "subdomains", "recon"],
  "aliases": ["scan_subdomains"],
  "representativeQueries": [
    "Enumerate subdomains via passive sources",
    "scan", "subdomains", "recon"
  ],
  "metadata": { "invoke": "python3 scripts/scan_subdomains.py", ... }
}

representativeQueries lead with the natural description phrase and pad with keywords, so both sentence-style and token-style agent queries hit. Every entry carries the field: when a resource offers no keywords, the file or skill name is split to pad the list, which is always 2–5 phrases. Entries keep exactly one of url/data (spec §3.4) and scalar-only metadata values, and the manifest envelope carries only specVersion/host/entries — the shape the official ai-catalog.schema.json validates.

The type media type is free-form per the spec, but entries use a standard name wherever one exists — application/mcp-server-card+json for MCP servers, text/markdown; profile="urn:air:agent-skills" for agent skills — so any conformant registry routes them without a local mapping. Plain local scripts have no standard type yet and keep the vendor one. Identifiers follow the spec's Appendix C form (urn:air:<publisher>:<namespace>:<name>), and the served manifest prepends a self-advert entry of type application/ai-registry+json so peers can discover this registry's search base URL. Search results carry a score in the spec's 0–100 relevance band (relevance only — ARD decouples trust into the trust manifest, §5).

Prior art / positioning

• HF Discover — reference implementation over the Hugging Face Hub; federated, semantic search.
• ARD spec — the standard itself (Apache-2.0).
• monid.ai — commercial take on the same pattern: a CLI/Skill teaching an agent to discover → inspect → run endpoints by intent. The difference: monid is a paid marketplace of third-party data endpoints behind an API key; ard-kit is the self-hosted, no-network, no-account version for resources you already own.

Known limitation: search here is lexical token overlap, not semantic ranking. That is deliberate — zero dependencies, and good enough for a few thousand private entries. The upgrade path is embeddings behind the same endpoint.

License

MIT.

Acknowledgements

Developed and hardened in Qoder — from the ARD v0.91 conformance rebuild through the ultra-review cycle.