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37
README.md
37
README.md
@@ -19,7 +19,7 @@ An MCP (Model Context Protocol) server that exposes a `knowledge_search` tool fo
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## Configuration
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Create a `.env` file (see `Settings` in `main.py` for all options):
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Create a `config.yaml` file or `.env` file (see `Settings` in `src/knowledge_search_mcp/config.py` for all options):
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```env
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PROJECT_ID=my-gcp-project
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@@ -42,16 +42,25 @@ SEARCH_LIMIT=10
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uv sync
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```
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### Run the MCP server (stdio)
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### Run the MCP server
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**Using the installed command (recommended):**
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```bash
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uv run python main.py
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# stdio transport (default)
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uv run knowledge-search-mcp
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# SSE transport for remote clients
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uv run knowledge-search-mcp --transport sse --port 8080
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# streamable-http transport
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uv run knowledge-search-mcp --transport streamable-http --port 8080
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```
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### Run the MCP server (SSE, e.g. for remote clients)
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**Or run directly:**
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```bash
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uv run python main.py --transport sse --port 8080
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uv run python -m knowledge_search_mcp.main
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```
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### Run the interactive agent (ADK)
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@@ -68,6 +77,12 @@ Or connect to an already-running SSE server:
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uv run python agent.py --remote http://localhost:8080/sse
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```
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### Run tests
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```bash
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uv run pytest
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```
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## Docker
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```bash
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@@ -80,8 +95,12 @@ The container starts the server in SSE mode on the port specified by `PORT` (def
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## Project structure
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```
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main.py MCP server, vector search client, and GCS storage helper
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agent.py Interactive ADK agent that consumes the MCP server
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Dockerfile Multi-stage build for Cloud Run / containerized deployment
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pyproject.toml Project metadata and dependencies
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src/knowledge_search_mcp/
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├── __init__.py Package initialization
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├── config.py Configuration management (Settings, args parsing)
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├── logging.py Cloud Logging setup
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└── main.py MCP server, vector search client, and GCS storage helper
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agent.py Interactive ADK agent that consumes the MCP server
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tests/ Test suite
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pyproject.toml Project metadata, dependencies, and entry points
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```
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