A Model Context Protocol (MCP) server that exposes Apache Lucene fulltext search capabilities with automatic document crawling and indexing. This
A Model Context Protocol (MCP) server that exposes Apache Lucene fulltext search capabilities with automatic document crawling and indexing. This server supports both STDIO transport (for Claude Desktop integration) and HTTP transport (for. Exposed over MCP by the mcpluceneserver mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Everything the assistant can do here goes through one of these:
Prerequisites — The Prerequisites tool exposed by this serversimpleSearch — Search the Lucene fulltext index using plain text keyword search. Special characters are treated as literals — no Lucene syntax knowledge requiredextendedSearch — Search the Lucene fulltext index using full Lucene query syntax. Supports Boolean operators, wildcards, proximity queries, and field-specificsemanticSearch — Pure KNN embedding-based semantic search. Finds semantically related documents even without exact keyword matches. Results are ordered by cosineprofileSemanticSearch — Debug tool for semantic search. Shows embedding time, cosine scores, matched chunks, and how many candidates passed the similarity threshold. UseprofileQuery — Analyze and debug simpleSearch / extendedSearch queries. Provides detailed insights into how Lucene processes your query, which terms contribute toInstallation goes through your MCP client rather than a global install: point it at mirkosertic42/mcpluceneserver on a container image and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
Among the AI and media services options, the useful question is rarely "what can it do" but "what does it cost you to run" — permissions, credentials, and how much of your context its toolset consumes. Mcpluceneserver's toolset — Prerequisites, simpleSearch, extendedSearch and 3 more — is a fair guide to whether it matches your workflow. It is maintained by mirkosertic; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Mcpluceneserver's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| Prerequisites | The Prerequisites tool exposed by this server. |
| simpleSearch | Search the Lucene fulltext index using plain text keyword search. Special characters are treated as literals — no Lucene syntax knowledge required. Uses BM25 with German and English stemming. |
| extendedSearch | Search the Lucene fulltext index using full Lucene query syntax. Supports Boolean operators, wildcards, proximity queries, and field-specific queries. Uses BM25 with German and English stemming. |
| semanticSearch | Pure KNN embedding-based semantic search. Finds semantically related documents even without exact keyword matches. Results are ordered by cosine similarity. Requires VECTOR_MODEL to be configured. |
| profileSemanticSearch | Debug tool for semantic search. Shows embedding time, cosine scores, matched chunks, and how many candidates passed the similarity threshold. Use this to tune similarityThreshold for your data. |
| profileQuery | Analyze and debug simpleSearch / extendedSearch queries. Provides detailed insights into how Lucene processes your query, which terms contribute to scoring, how filters affect results, and where optimization opportunitie |
{
"mcpServers": {
"mcpluceneserver": {
"command": "docker",
"args": ["run", "-i", "--rm", "mirkosertic42/mcpluceneserver"]
}
}
}Add to claude_desktop_config.json, then restart Claude Desktop.
Build a programmable telecommunications stack for connecting telephony services with the Internet via a cloud-based utility.
Search built for AI, not humans — semantic web search that returns model-ready content, plus code context.
Answers, not links — delegate questions to Perplexity's search-grounded models and get cited responses back.
Give your assistant a voice — text-to-speech, voice cloning and audio tools from the ElevenLabs API.
Give your assistant a real code sandbox — isolated cloud VMs for actually running the code it writes.
The ML hub in your context window — search models, datasets, papers and run Spaces from the official server.