GemFireMCPServer MCP Server

@jomartin-999

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Basic information

About GemFireMCPServer MCP Server

No overview available yet

#gemfire#mcp #server #vector-search#financial-documents

GemFireMCPServer MCP Server config

No standard config provided

This server doesn't expose a parseable MCP config block in its README. See the repository for install instructions.

Repository

GemFireMCPServer MCP Server tools

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Overview

What is GemFireMCPServer?

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.

How to use GemFireMCPServer?

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.

Key features of GemFireMCPServer

MCP-compliant endpoints for document ingestion, listing, and querying
Local ONNX-based embeddings via Spring AI
Vector and metadata storage in VMware GemFire
File metadata stored in a dedicated GemFire region
Fast, in-memory semantic search

Use cases of GemFireMCPServer

Upload financial documents and automatically chunk, embed, and store them in GemFire
Browse metadata of all stored documents from an MCP client
Ask natural‑language questions about documents and receive RAG‑generated answers

Frequently asked questions

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.

How are embeddings generated?

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.

Where are documents and metadata stored?

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.

How is the server invoked by the client?

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.

What authentication is needed for GemFire dependencies?

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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