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research-hub

research-hub

by WenyuChiou
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

web_search

Retrieve web content such as blog posts, documentation, news, and GitHub repositories. Use this when your research requires information beyond academic papers.

Instructions

General web search (blog posts / docs / news / GitHub READMEs).

Use alongside auto_research_topic when the user's need extends beyond peer-reviewed papers and into official docs, engineering blogs, or news.

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": "web_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
max_resultsNo
providerNoauto

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.7/5.0
Behavior3/5

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

No annotations are provided, so the description must convey behavioral traits. It describes the tool as performing general web search and lists content types, but doesn't disclose specifics like search engine used, rate limits, or how results are returned. However, typical web search behavior is predictable, and the output schema covers return format.

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: two sentences, each serving a distinct purpose—what it does and when to use it. No superfluous content.

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

Completeness2/5

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

Despite having an output schema, the description fails to explain critical parameters like 'provider' and 'max_results'. For a web search tool, these are important for proper use. The description is incomplete for a task this complex.

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

Parameters1/5

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

The input schema has 0% description coverage, and the description adds no information about parameters. Users are left to infer meanings of 'query', 'max_results', and 'provider' from their names alone. This is a significant gap.

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 explicitly states the tool performs general web search and lists specific content types (blog posts, docs, news, GitHub READMEs). It distinguishes itself from the sibling tool auto_research_topic by noting it covers non-peer-reviewed sources.

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 provides explicit guidance on when to use this tool: alongside auto_research_topic when the user needs content beyond peer-reviewed papers. This directly addresses tool selection.

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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