What runtime does GemFireMCPServer require?
Java 17+, Gradle (or Maven), VMware GemFire 10.x+, and the GemFire VectorDB extension are required. An MCP client like Claude Desktop is used to interact with the server.
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GemFireMCPServer is a Spring Boot–based MCP server that integrates VMware GemFire with Spring AI to enable fast, vector-based semantic search over financial documents. It is designed for developers using MCP clients like Claude Desktop to build Retrieval-Augmented Generation (RAG) workflows.
Clone the repository, configure GemFire artifact access credentials from Broadcom, set application.properties with GemFire cluster details and ONNX model paths, build with Gradle, start a GemFire cluster with the VectorDB extension, then register the server as an MCP tool in Claude Desktop. Three tools become available: add_financial_doc, list_available_financial_docs, and search_financial_docs.
Java 17+, Gradle (or Maven), VMware GemFire 10.x+, and the GemFire VectorDB extension are required. An MCP client like Claude Desktop is used to interact with the server.
The server uses a local ONNX‑exported version of the `sentence-transformers/all-MiniLM-L6-v2` model. You must export the model with `optimum-cli` and place `model.onnx` and `tokenizer.json` on the classpath.
All vector embeddings are stored in a GemFire vector index named `financialDocuments`. File metadata (name, size) is kept in a separate GemFire region defined by the `gemfire.region.docsMetadata` property.
The server uses the MCP STDIO transport, so it is launched as a subprocess by the MCP client (e.g., via `java -jar` in Claude Desktop’s configuration file). No HTTP server is involved.
Access to the GemFire artifact repository requires a Broadcom customer support username and a personal access token, which are configured in `gradle.properties` or Maven’s `settings.xml`.
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