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serper-search-mcp-server

serper-search-mcp-server

by smjahid012
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
Hybrid

deep_research

Breaks a research question into sub-queries, runs multiple Google searches, and synthesizes a cited report using your configured LLM.

Instructions

AI-powered deep research tool. Breaks your question into sub-queries, runs multiple Google searches, then synthesizes a cited research report using your LLM (OpenRouter or Gemini). Requires OPENROUTER_API_KEY or GEMINI_API_KEY environment variable.

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": "deep_research"
  }
}

Input Schema

NameRequiredDescriptionDefault
depthNoResearch depth: "basic" (3 sub-queries, fast), "standard" (5 sub-queries, balanced), "deep" (8 sub-queries, exhaustive). Default: "standard".standard
queryYesResearch question or topic to investigate.
countryNoCountry code for search localisation. Default: "US".US

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv4.0.1

TDQS

A3.8/5.0
Behavior4/5

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

With no annotations, the description carries the full load and does well: it discloses the internal pipeline (sub-queries → multiple searches → LLM synthesis), that output is a cited report, and that an OPENROUTER_API_KEY or GEMINI_API_KEY is required. It stops short of noting latency, cost implications of multiple searches plus LLM calls, or failure behavior when the key is missing.

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?

Three tight sentences, front-loaded with purpose then mechanism then prerequisite. Nothing is redundant and the most critical constraint (API key) is not buried.

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 no-annotation, no-output-schema tool with fully documented params, the description covers purpose, pipeline, and the key requirement. Minor gaps remain around latency/cost and the exact shape of the returned report, but an agent has enough to invoke it correctly.

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

Parameters3/5

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

Schema coverage is 100% and the depth enum values are fully documented in the schema, so baseline 3 applies. The description adds nothing about depth, country, or the query parameter beyond what the schema already says.

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?

States a specific verb and resource ('deep research tool') and explains the mechanism: sub-query decomposition, multiple Google searches, synthesis into a cited report. This implicitly separates it from single-shot siblings like search or search_web, though it never names an alternative explicitly.

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?

Usage is only implied — the word 'deep' and the multi-search pipeline suggest it suits complex questions that warrant synthesis rather than a single lookup, but there is no explicit when-to-use/when-not guidance and no comparison to search or search_many. An agent must infer the cost/latency tradeoff itself.

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

Deploy Server

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