Zotero Chunk RAG
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| GEMINI_API_KEY | Yes | Your Gemini API key. Can be provided via this environment variable or in the config.json file. |
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 |
|---|---|
| tasks | {
"list": {},
"cancel": {},
"requests": {
"tools": {
"call": {}
},
"prompts": {
"get": {}
},
"resources": {
"read": {}
}
}
} |
| tools | {
"listChanged": true
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| search_papersA | Semantic search over research paper chunks. Returns relevant passages with surrounding context. Args: query: Natural language search query top_k: Number of results (1-50) context_chunks: Adjacent chunks to include (0-3) year_min: Minimum publication year filter year_max: Maximum publication year filter Returns: List of results with passage text, context, and metadata |
| search_topicA | Find the most relevant papers for a topic, deduplicated by document. Searches across all chunks, then groups by paper. Each paper is scored by both its average chunk relevance (overall topical fit) and its best single chunk (strongest individual passage). Results are sorted by average score. Args: query: Natural language topic description num_papers: Number of distinct papers to return (1-50) year_min: Minimum publication year filter year_max: Maximum publication year filter Returns: List of per-paper results with scores and best passage |
| get_passage_contextA | Expand context around a specific passage. Use after search_papers to get more context. Args: doc_id: Document ID from search results chunk_index: Chunk index from search results window: Chunks before/after to include (1-5) |
| get_index_statsB | Get statistics about the indexed collection. |
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 4 tools
The tools are mostly distinct in purpose: get_index_stats provides collection statistics, search_papers finds specific passages, search_topic finds relevant papers by topic, and get_passage_context expands context around a passage. However, search_papers and search_topic both involve semantic search over paper content, which could cause some confusion about when to use each, though their descriptions clarify the difference (passage-level vs. paper-level results).
All tool names follow a consistent snake_case pattern with clear verb_noun structures: get_index_stats, get_passage_context, search_papers, and search_topic. The naming is predictable and readable, with no mixing of conventions or stylistic deviations.
With 4 tools, the count is reasonable for a RAG-focused server, covering key operations like searching, context expansion, and statistics. It might be slightly thin for broader document management tasks (e.g., no indexing or update tools), but it's well-scoped for its apparent purpose of querying and exploring a research paper collection.
The toolset covers core retrieval and exploration functions (search, context expansion, stats) but has notable gaps for a full RAG or document management system. There are no tools for indexing, updating, or deleting documents, and operations like filtering by metadata beyond year are limited. This could lead to dead ends for agents needing to modify or fully manage the collection.