Multi-Agent Deep Researcher MCP

quick_search

Search the web and return structured snippets with source URLs. Set result count and engine to retrieve relevant citations for research.

Instructions

Conduct a fast web search and return structured snippets with source URLs.

Args:
    query: The search term or question.
    max_results: Maximum number of links to return (default: 5, max: 15).
    search_engine: Search engine to use ('auto', 'linkup', or 'duckduckgo').

Returns:
    Formatted search results with titles, snippets, and URLs.

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": "quick_search"
  }
}

Input Schema

Name	Required	Default
`query`	Yes
`max_results`	No
`search_engine`	No	auto

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 6, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It explains what the tool does and what it returns, but it does not describe how 'auto' selects a search engine, potential failures, rate limits, or other behavioral nuances.

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 and well-structured, front-loading the purpose and using clear Args/Returns sections. Every sentence contributes useful information without unnecessary filler.

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?

For a simple tool, the description includes all invocation-relevant details: required query, optional parameters with defaults, engine choices, and return format. It is only missing explicit usage context relative to the sibling tools.

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%, but the description fully compensates by documenting all three parameters: query, max_results with default and max, and search_engine with its enum options. This is exactly the information an agent needs beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific action ('fast web search') and resource (the web), and specifies the return format (structured snippets with source URLs). It is distinguishable from the sibling research-report tools, though it does not explicitly name or contrast them.

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 word 'fast' implies it is for quick searches rather than deep research, but there is no explicit guidance about when to choose this tool over siblings like deep_research. No exclusions or alternative-selection criteria are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Deploy Server