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zotero-mcp

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Python
Hybrid

Semantic Search

semantic_search

Search your Zotero library by meaning, not exact wording. Enter a topic, claim, or question to find relevant items and see the matching passages.

Instructions

Find items by meaning using the vector index. Each hit shows the passage that matched.

Complements search_library (exact-word matching): use this when you know the idea but not the wording. Phrase the query as a topic, claim, or question rather than keywords. Results are reranked by a cross-encoder; matched_in says whether the title/abstract or a fulltext passage matched, and evidence counts matching passages. Filters narrow by type, year range, or collection. Requires the index to have been built (zotero-mcp index build).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax items to return.
queryYesA natural-language description of what you are looking for — a topic, claim, method, or question. Matches by meaning, not exact words.
year_toNoOnly items published in this year or earlier.
item_typeNoRestrict to one Zotero item type, e.g. 'journalArticle', 'book', 'thesis'.
year_fromNoOnly items published in this year or later.
collection_keyNoOnly items in this collection (key from `list_collections`).

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.7/5.0
Behavior4/5

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

No annotations are provided, so the description carries the burden. It discloses the prerequisite index build, reranking behavior, and the meaning of output fields, though it does not discuss side effects or failure modes.

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

Conciseness5/5

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

The description is compact, well-organized, and free of fluff. It front-loads the core purpose and then adds usage, output, filtering, and prerequisite details in a logical order.

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

Completeness4/5

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

There is no output schema, but the description compensates by naming output fields (`matched_in`, `evidence`). It could mention that results are sorted by relevance or note pagination, but this is a minor gap.

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 six parameters are described in the schema, and the description adds valuable context for the query parameter by explaining how to phrase it. Filter parameters are clearly explained with examples.

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?

Clearly states the tool finds items by meaning using a vector index, and distinguishes it from exact-word search in the same family. The purpose is immediately obvious and specific.

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?

Provides explicit guidance on when to use it ('know the idea but not the wording'), how to phrase queries ('topic, claim, or question rather than keywords'), and mentions reranking and index build 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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