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SSX360

matrixscroll

by SSX360
OverviewSchemaRelated ServersScoreDiscussions
Python
Local

scan_research_radar

Search for academic papers and machine learning models relevant to your project workspace. Query live from arXiv and Hugging Face or use cached results.

Instructions

Surface academic papers, models, and AI releases relevant to the workspace.

This tool is read-only and assists with literature mapping and model selection for agent tasks.

Parameters:
    workspace (str): The absolute path to the local project workspace.
    goal (str, optional): The research topic or machine learning goal. Defaults to "".
    limit (int, optional): The maximum number of papers or models to return. Defaults to 4.
    live (bool, optional): If True, queries arXiv and Hugging Face API live.
                           If False (default), uses local cached results.

Returns:
    dict[str, Any]: A dictionary listing relevant papers, machine learning models, and source metadata.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
goalNo
liveNo
limitNo
workspaceYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Tool Definition Quality

A4.1/5.0
Behavior3/5

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

With no annotations provided, the description carries the full burden. It declares the tool as read-only, explains the live parameter for API queries vs caching, and mentions return type. It lacks details on error handling, required permissions, or behavior when workspace path is invalid.

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 concise and well-structured: a one-line summary, a usage note, a parameter list with clear descriptions, and a return statement. Every sentence adds value without redundancy or verbosity.

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 0% schema coverage and no annotations, the description covers purpose, parameters, and return type. It leverages the existing output schema to avoid detailing returns. However, it omits edge-case behaviors like invalid workspace paths or empty goal defaults, so it is not fully exhaustive.

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?

Schema description coverage is 0%, meaning the input schema provides no parameter explanations. The tool description compensates fully by describing each parameter: workspace (absolute path), goal (research topic), limit (max results), live (API query toggle). This adds essential meaning beyond the schema.

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 surfaces academic papers, models, and AI releases relevant to the workspace, using a specific verb (surface) and resource. It distinguishes from siblings like scan_market_radar which likely focuses on market data.

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 notes the tool is read-only and assists with literature mapping and model selection, providing some context. However, it does not explicitly compare to sibling tools or state when not to use it, leaving usage guidance implied rather than explicit.

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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