MCP Elasticsearch Demo
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| PORT | No | Port for the HTTP server. | |
| MCP_API_KEY | No | Bearer token used for authentication by the HTTP server (src/httpServer.js). | |
| STORE_BACKEND | No | Backend store to use. Default is 'memory'. Set to 'elasticsearch' to use a real Elasticsearch instance. | memory |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_documentsA | Wyszukiwanie hybrydowe (leksykalne BM25 + semantyczne) w bazie instrukcji, filmów instruktażowych, PDF-ów, DOCX-ów i zdjęć. Zwraca najlepiej dopasowane fragmenty wraz ze źródłem; dla filmów dodatkowo znacznik czasu konkretnego segmentu, do którego warto skierować użytkownika. |
| get_documentA | Zwraca pełną treść i metadane jednego dokumentu po jego id (z wyników search_documents). |
| list_facetsA | Zwraca dostępne wartości danego pola (doc_type, product albo tags) wraz z liczbą dokumentów — przydatne, żeby dowiedzieć się jakich filtrów można użyć w search_documents, zamiast zgadywać nazwę produktu czy tagu. |
| find_similarA | Zwraca dokumenty najbardziej semantycznie podobne do podanego (po id) — np. "pokaż więcej takich jak ten". |
| get_video_segmentC | Zwraca transkrypt fragmentu filmu instruktażowego z podanego zakresu czasu (w sekundach). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool has a distinct retrieval purpose: search_documents queries by text, find_similar queries by example id, list_facets enumerates filter values, get_document fetches a full doc by id, and get_video_segment pulls a time-bounded transcript. The main mild overlap is search_documents vs find_similar (both return ranked recommendations), but the input mode (query vs id) and descriptions differentiate them adequately.
All five names follow a consistent snake_case verb_noun pattern (get_video_segment, search_documents, get_document, list_facets, find_similar). Verb choice is predictable and readable across the set.
Five tools is well-scoped for a hybrid search/retrieval server, covering search, drill-down, filtering discovery, similarity, and a media-specific accessor. Every tool earns its place with no redundant filler.
The retrieval lifecycle is well covered: search, fetch full content, discover facets, find similar, and extract a video segment. It lacks any indexing/write operations (create/update/delete or list-all), which may be intentional for a read-only demo but leaves the surface one-sided.