Reference Implementations¶

Hugging Face Discover Tool¶

The Hugging Face Discover Tool provides search access to thousands of Skills, ML Applications, and MCP Servers — on Hugging Face - or any other ARD compliant service.

Hugging Face CLI (`hf`)¶

discover is built into the Hugging Face CLI (hf). To get started:

# Install the Hugging Face CLI tool:
uv tool install huggingface_hub

# Search for resources to train a model
hf discover search "Fine tune a language model"

# Find MCP Servers to generate an image
hf discover search "Generate an image" --json --kind mcp

# Search other registries
hf discover search "Purchase aeroplane tickets" --registry-url <catalog-url>

# Navigate a federated catalog from a website
hf discover navigate <web-url> "Research biomedical datasets"

REST and MCP API Access¶

Query the Hugging Face catalog service directly via:

The REST API at: https://huggingface-hf-discover.hf.space/search
MCP at: https://huggingface-hf-discover.hf.space/mcp

GitHub Agent Finder¶

GitHub's Agent Finder is a discovery service for agentic resources — Skills, tools, and MCP servers — reachable over HTTPS at https://agentfinder.github.com/api/v1.

GitHub Copilot¶

GitHub Copilot can search it directly: add Agent Finder as a remote MCP tool (or as custom instructions), then ask Copilot to find a capability for your task and it returns ranked matches you choose to install. See Connect GitHub Copilot for the full setup — it uses this same endpoint as its example.

HTTP API¶

Call search directly at POST https://agentfinder.github.com/api/v1/search. The MCP endpoint is https://agentfinder.github.com/api/v1/mcp.

Cisco AI Catalog¶

The AGNTCY Agent Directory reference implementation of ARD is deployed by the Cisco AI Catalog. The catalog can be pulled from ai-catalog.outshift.io/.well-known/ai-catalog.json. It supports secure verification through trust manifests, so clients can validate publisher identity and resource integrity before use.

1. Pull the catalog manifest¶

curl -sS https://ai-catalog.outshift.io/.well-known/ai-catalog.json | jq '.specVersion, .host.displayName'

2. Discover A2A cards¶

curl -sS 'https://ai-catalog.outshift.io/v1/agents?filter=type%3Dapplication%2Fa2a-agent-card%2Bjson' \
    | jq -r '.results[] | "\(.displayName)\t\(.data.card_data.url // .identifier)"'

3. Search by card type and extract trust details¶

curl -sS 'https://ai-catalog.outshift.io/v1/agents?filter=type%3Dapplication%2Fmcp-server-card%2Bjson' \
    | jq -r '.results[] | {displayName, identity: .trustManifest.identity, identityType: .trustManifest.identityType, cardUrl: .data.card_data.url} | @json'

Ora Directory¶

The Ora Directory is an ARD discovery service over products and services that agents use on behalf of users, run by Ora. Ora scans each product for agent-readiness — static checks against its docs, llms.txt, registries, and public APIs, plus live agent runs that attempt to use it end to end — and serves the results over the ARD protocol, alongside the MCP servers, Skills, and OpenAPI specs detected on each product, plus payable x402/MPP HTTP endpoints with per-call pricing, indexed from the public Bazaar registry. Every product entry carries its agent-readiness scorecard as a signed trust attestation, so a client can weigh not only whether a resource matches the task, but whether it has been observed to work for agents.

Ora's publisher manifest at ora.ai/.well-known/ai-catalog.json describes Ora's own resources and advertises the registry: its application/ai-registry+json entry points at https://ora.ai/api/ard, which serves a self-describing descriptor listing the endpoints. The index itself is queried through those endpoints.

Search and browse¶

The registry implements the full protocol surface — POST /search, POST /explore, and GET /agents with the spec's filter expressions and orderBy — and returns referrals to peer registries.

# Find products for a task
curl -sS -X POST https://ora.ai/api/ard/search \
  -H 'content-type: application/json' \
  -d '{"query":{"text":"send transactional email"},"pageSize":5}' \
  | jq -r '.results[] | "\(.displayName)\t\(.url)"'

# Browse just the MCP servers in the index
curl -sS -G https://ora.ai/api/ard/agents \
  --data-urlencode "filter=type = 'application/mcp-server-card+json'" \
  --data-urlencode "pageSize=5" \
  | jq -r '.items[].displayName'

Verify a scorecard¶

Each result's trustManifest.attestations[] references the product's agent-readiness scorecard, signed as a detached Ed25519 JWS and verifiable against the JWKS at ora.ai/.well-known/jwks.json:

curl -sS https://ora.ai/api/ard/attestation/resend.com \
  | jq '{subject, score, grade, issuer}'

MCP¶

Ora is also reachable as an MCP server at https://ora.ai/api/mcp (streamable HTTP); its discover_products, get_score, and search_capabilities tools query the same index.