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Metis · Librarian — Propose Library Organization

propose_library_organization

Cluster papers by topic and propose an AI-generated collection structure with suggested names and paper counts, helping you organize a research library into meaningful groups.

Instructions

Cluster papers by topic and propose an AI-generated collection structure.

Uses abstracts and titles to embed papers, then clusters them with k-means.
Returns a proposed collection structure with suggested names and paper counts.

Args:
    n_clusters: Number of topic clusters. 0 = auto-detect (sqrt of library size).
    min_papers: Minimum papers per cluster to report (default 3).

Input Schema

NameRequiredDescriptionDefault
min_papersNo
n_clustersNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior3/5

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

With no annotations, the description discloses the algorithm and parameter effects but omits side effects, permissions, or whether it is read-only.

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 very concise: three short paragraphs covering purpose, method, and parameters without extraneous details.

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 the presence of an output schema, the description adequately covers the tool's behavior and parameters, though it could mention prerequisites or safety.

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?

The description adds meaning to both parameters (n_clusters auto-detect at 0, min_papers default 3) beyond the schema, which has 0% coverage.

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 it clusters papers and proposes a collection structure, distinguishing it from sibling tools like search or list operations.

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

Usage Guidelines3/5

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

The description explains how the tool works (k-means on embeddings) but does not explicitly state when to use it or when to prefer alternatives like search_library.

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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