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Zotero Chunk RAG

Zotero Chunk RAG

by ccam80
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_topic

Find relevant academic papers by searching document chunks, grouping by paper, and scoring based on topical fit and strongest passages.

Instructions

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

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_topic"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
num_papersNo
year_minNo
year_maxNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.2/5.0
Behavior3/5

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

With no annotations provided, the description carries full burden. It discloses key behavioral traits: deduplication by document, scoring methodology (average and best chunk relevance), sorting by average score, and filtering by year. However, it doesn't mention rate limits, authentication needs, error conditions, or pagination behavior for large result sets.

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 well-structured and appropriately sized. It begins with the core purpose, explains the deduplication and scoring methodology, lists parameters with clear semantics, and describes the return format. Every sentence adds value with zero wasted words.

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?

Given 4 parameters with 0% schema coverage and no annotations, the description does an excellent job explaining parameter semantics and tool behavior. The presence of an output schema means return values don't need explanation. However, for a search tool with scoring complexity, it could benefit from mentioning performance characteristics or limitations.

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

Parameters4/5

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

Schema description coverage is 0%, so the description must compensate. It provides clear semantic meaning for all 4 parameters: 'query' as natural language topic, 'num_papers' as count of distinct papers with range, and year filters as publication year bounds. This adds substantial value beyond the bare schema, though it doesn't explain null handling for year 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 states the tool's purpose: 'Find the most relevant papers for a topic, deduplicated by document.' It specifies the verb 'find' and resource 'papers', and distinguishes from sibling 'search_papers' by emphasizing deduplication and scoring methodology (average vs best chunk).

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: for finding papers by topic with deduplication and scoring. However, it doesn't explicitly state when NOT to use it or directly compare with sibling 'search_papers', which might be an alternative for paper searches without the specific scoring/deduplication approach described here.

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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