The MCP Server Integration Planner

#ACTION
Design an MCP (Model Context Protocol) server specification for integrating {{target_system}} with AI agents.

#PURPOSE
MCP servers let agents access your systems through standardized tools and resources. A well-designed server makes the difference between an agent that works and one that hallucinates API calls.

#EXPECTATION
Produce a complete MCP server spec:

## 1. Resource Catalog
List each resource the server will expose:
- Resource URI pattern (e.g., `file:///project/{path}`)
- What it returns (schema or description)
- When an agent would request it
- Caching strategy (static, TTL, never)

## 2. Tool Definitions
For each tool the agent can call:
- Tool name (verb-noun: `create_issue`, `query_logs`)
- Input schema (JSON Schema with required fields)
- Output schema
- Side effects (read-only vs. mutating)
- Idempotency key if applicable
- Rate limits

## 3. Authentication & Authorization
- Auth method (OAuth, API key, mTLS, none)
- How credentials are passed to the server
- Scope/permission model (what agents can access what)

## 4. Deployment Architecture
- Transport: stdio / SSE / HTTP
- Hosting: local / container / serverless / managed
- Scaling considerations (stateless? connection pooling?)
- Observability: logging, metrics, tracing

## 5. Security Review
- Input validation approach
- Injection risks (SQL, command, prompt)
- Data exposure boundaries
- Audit logging requirements

## 6. Implementation Checklist
- [ ] Resource handlers
- [ ] Tool handlers
- [ ] Auth middleware
- [ ] Error handling
- [ ] Tests (unit + integration)
- [ ] Documentation for agent developers

#CONTEXT
Target system: {{target_system}} (e.g., PostgreSQL, GitHub, Jira, internal API)
Use case: {{what_agents_need_to_do}}
Existing infrastructure: {{infra}}
Team expertise: {{team_skills}}