MCP Test MCP

--- story_id: "0007" title: "Implement resource testing tools" created: "2025-10-09" status: done dependencies: ["0003", "0004"] estimated_complexity: "medium" tags: ["tools", "resources", "phase1"] - status: in-progress timestamp: 2025-10-09T21:53:54Z - status: ready-for-review timestamp: 2025-10-09T22:16:10Z - status: done timestamp: 2025-10-10T01:13:01Z --- # Story 0007: Implement resource testing tools ## Description Implement the two MCP tools for testing target server resources: list_resources and read_resource. These tools enable AI assistants to discover and read resources from connected MCP servers. ## Acceptance Criteria - [x] `src/mcp_test_mcp/tools/resources.py` created - [x] `list_resources()` tool implemented - [x] `read_resource(uri: str)` tool implemented - [x] Both tools registered with FastMCP server - [x] `list_resources()` returns complete resource metadata (uri, name, description, mimeType) - [x] `read_resource()` returns resource content with metadata - [x] Error handling for not_connected, resource_not_found - [x] Statistics updated after each resource read - [x] Unit tests for each tool (82%+ coverage) - [x] Integration test with mock MCP server providing resources ## Technical Notes **list_resources response:** ```python { "success": true, "resources": [ { "uri": "config://settings", "name": "Application Settings", "description": "Configuration settings", "mimeType": "application/json" } ], "metadata": { "total_resources": 2, "retrieved_at": "...", "request_time_ms": 98 } } ``` **read_resource response:** ```python { "success": true, "resource": { "uri": "config://settings", "mimeType": "application/json", "content": "{...}" }, "metadata": { "content_size": 24, "request_time_ms": 87 } } ``` ## AI Directives **IMPORTANT**: As you work through this story, please mark checklist items as complete `[x]` as you finish them. This ensures that if we need to pause and resume work, we have a clear record of progress. Update the `status` field in the frontmatter when moving between stages (in-progress, ready-for-review, done, blocked). Include complete metadata to help AI understand resource structure and content.