"Java command reference and usage" matching MCP servers:

Lets MCP clients search vetted public listings and their own private library of coding-agent sessions, load a saved session as bounded starting context, and save the current transcript privately, reusing an existing command-line sign-in. Sessions are scrubbed and framed as untrusted reference material, and acquiring a paid listing never completes a purchase automatically.

Deterministic code-graph (GraphRAG) over your repo for LLM agents — local-first, git-native, zero-infra, served via MCP. Python, TS/JS, Rust, Go, Java, C#.

Provides AI coding assistants with context optimization tools including targeted file analysis, intelligent terminal command execution with LLM-powered output extraction, and web research capabilities. Helps reduce token usage by extracting only relevant information instead of processing entire files and command outputs.

MCP server for searching, browsing, and analyzing decompiled Minecraft source code locally. Supports symbol lookup, text search, reference lookup, and lightweight RAG.

A Model Context Protocol server that generates prompts based on Git repository content, including a command to generate PR descriptions from diffs.

Query Langfuse traces, schema and datasets, scores and metrics, debug exceptions, analyze sessions, and manage prompts. Full observability toolkit for LLM applications.

Enables querying Langfuse analytics, cost metrics, and usage data across multiple projects. Provides tools for trace analysis, model/service cost breakdowns, and daily usage trends through natural language queries.

Enables discovering and independently validating scholarly metadata from extracted research text using Crossref, OpenAlex, DataCite, and optional read-only Zotero checks, returning ranked and verified reference candidates.

Enables local-first context retrieval and management from JSONL and Markdown sources through MCP, with a rebuildable full-text search index and support for stable reference pagination.

Ranks and filters retrieved context locally on your machine to reduce LLM token usage, cutting context tokens by ~67% while preserving relevant passages.

Reference MCP server for OCL, an open standard giving AI agents structured, scoped business meaning for Odoo data. Serves published example context packs via get_context, resolve_noun, and explain_field (stdio, Python, Apache-2.0).

Enables AI assistants to perform intelligent web searches using the Baidu Wenxin API, supporting multiple models, search modes, and providing search results with reference sources.

A reference implementation for creating an MCP server supporting Streamable HTTP & SSE Transports with OAuth authorization, allowing developers to build OAuth-authorized MCP servers with minimal configuration.

An MCP server that provides powerful search capabilities for Jewish texts and literature. This server enables Large Language Models to search and reference Jewish texts through a standardized interface.

Natural Language Analytics MCP server for Apache Druid. With this enterprise ready server in Java, one can query Apache Druid by natural language queries. Furthermore, the complete management like data loading of the Time Series Database Druid can be done with natural language commands.

Enables coding agents to retrieve the code a change touches and the decisions behind it, with separate code and decision search, symbol explanations, and import/reference path finding.

Enables symbol-level codebase retrieval by returning a definition's source span and usage sites, avoiding whole-file reads to reduce token context.

Enables semantic and keyword search of Ceph documentation with version-aware, component-scoped indices, supporting command lookup and code example retrieval.

Enables MCP clients to interact with a local-first research knowledge workbench, supporting literature search, evidence-grounded Q&A, and reference export.

Enables storing large command outputs, files, and web pages in a local SQLite index and retrieving only the relevant passages Claude asks for, reducing context usage.