MCP Server Discovery & Integration Protocol — SKILL.md for Agent-Native Tool Discovery
$0.03 / access
SKILL.md Protocol
mcp-discovery-skill is a SKILL.md behavioral protocol that teaches AI agents how to systematically discover, evaluate, and integrate MCP (Model Context Protocol) servers into their tool pipelines. From registry navigation to transport configuration to fallback handling — the complete MCP integration decision protocol for agents building on the Model Context Protocol ecosystem.
Discovery before purchase: The protocol includes a structured pre-purchase evaluation phase. Agents learn to inspect MCP server capabilities, assess trust signals, and validate transport compatibility before committing to integration — reducing integration failures and wasted cycles.
Protocol Phases
Phase 1 — Registry Navigation
Navigate MCP server registries (mcp.so, GitHub awesome-mcp lists, direct vendor registries). Filter by capability category, transport type, and maintenance status. Build a shortlist of candidates matching the agent's tool requirements.
Phase 2 — Capability Evaluation
For each candidate server: parse the MCP manifest, enumerate available tools and resources, assess capability completeness against task requirements, and score against evaluation criteria.
Phase 3 — Trust Scoring
Evaluate trust signals: maintainer reputation, GitHub star/fork count, last commit recency, open issue count, security disclosure history. Assign a trust score (0–100) and apply minimum threshold gates before integration.
Phase 4 — Transport Configuration
Configure the selected transport (stdio, SSE, HTTP/streaming). Handle authentication headers, connection timeouts, and protocol version negotiation. Validate the connection with a capability probe before registering the server.
Phase 5 — Fallback Handling
Define degraded-mode behavior when an MCP server is unavailable or returns unexpected responses. Implement retry logic with exponential backoff, fallback to alternative servers in the shortlist, and graceful task continuation without the tool.
Transport Types Covered
| Transport | Use Case | Configuration |
|---|
| stdio | Local processes, Claude Desktop, Cursor IDE | Command path, args, env vars |
| SSE (Server-Sent Events) | Remote servers, persistent connections | Endpoint URL, auth headers |
| HTTP/streaming | Cloud-hosted MCP services, agent pipelines | Base URL, bearer token, timeout |
Agent Use Cases
- Claude Code agents — discover and integrate MCP servers for web scraping, database access, or API calls without hardcoding specific tools
- Cursor background agents — dynamically extend tool availability by discovering MCP servers matching the current task's requirements
- Agent pipeline builders — systematically evaluate 10+ MCP candidates and select the best-fit server for each tool category in a production pipeline
- Resilient agent deployments — implement fallback chains so agents continue operating when preferred MCP servers are unavailable
How to Access via x402
- Free preview:
GET https://clawmerchants.com/v1/preview/mcp-discovery-skill — inspect Phase 1 (Registry Navigation) before paying
- Probe:
GET https://clawmerchants.com/v1/data/mcp-discovery-skill → HTTP 402 with USDC payment details
- Pay: Send 0.03 USDC on Base L2 (chain ID 8453) to the provider wallet in the 402 response
- Receive: Resend with
X-PAYMENT: <base64 proof> → HTTP 200 with full 5-phase protocol text
Pricing
$0.03 USDC per access — no subscription, no API key, no account. Load the protocol once per agent session where MCP discovery is needed. The structured evaluation framework saves more time in integration debugging than the access cost many times over.
ClawMerchants — MCP server discovery protocol for AI agents — x402 + USDC + Base L2