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zotero_semantic_search

Search your Zotero library using natural language queries to find papers by semantic similarity, helping you discover relevant research on specific topics.

Instructions

Prioritized topic-search tool. Find papers by semantic similarity to a query using AI embeddings — the BEST tool for finding papers on a topic (e.g. 'papers about mindfulness-based therapy'), far more efficient than scanning collection items or reading abstracts. Works across the entire active library. query: the topic or concept; natural-language phrases work well. limit: max results (default 10). filters: optional metadata filters as a dict (e.g. {'itemType': 'journalArticle', 'year': '2023'}); also accepts a JSON string. Requires the semantic search database to be POPULATED — run zotero_update_search_database first if you just installed the server or added new items; check readiness with zotero_get_search_database_status. Available only when the [semantic] optional dependency is installed (pip install zotero-mcp-server[semantic]). Example: zotero_semantic_search(query='mindfulness-based cognitive therapy for depression', limit=5).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query text - can be concepts, topics, or natural language descriptions
limitNoMaximum number of results to return (default: 10)
filtersNoOptional metadata filters as dict or JSON string. Example: {"item_type": "note"}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden. It discloses that the tool operates across the entire active library, requires a populated database, and that filters accept dict/JSON. It does not mention side effects (though none expected for a search) or rate limits. Overall, it provides sufficient behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with a bold opening statement, then usage guidance, parameter descriptions, and an example. It is slightly verbose (e.g., 'the BEST tool' is promotional but tolerable). The front-loading of key purpose is effective.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity and the presence of an output schema, the description covers all essential aspects: purpose, when to use, prerequisites, parameter details, and an example. It does not rely on the schema for behavioral or usage context, making it self-sufficient and complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

All parameters are described in the schema (100% coverage). The description adds valuable clarification beyond the schema: it explains query as 'topic or concept' and that natural-language phrases work well; limit as max results; filters as optional metadata filters with dict/JSON string example. An overall example integrates all parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly identifies it as a 'topic-search tool' using semantic similarity, and explicitly differentiates it from other search tools (e.g., scanning collections). It states it is the 'BEST tool for finding papers on a topic' and provides a concrete example.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use ('topic-driven searches'), what the tool does ('find papers by semantic similarity'), and important preconditions: the semantic search database must be populated before use (with reference to zotero_update_search_database and zotero_get_search_database_status). It also mentions the optional dependency requirement.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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