Skip to main content
Glama

Search + Parallel Extract

search_extract
Read-only

Search Google and extract top results in parallel, using concise abstracts for a cheap survey or full texts for detailed content, to save tokens by fetching only needed information.

Instructions

One-shot Google search + parallel extract of the top results. Default mode="abstract" returns SERP enriched with ~1500-char abstracts per result -- a cheap survey of what the top results actually contain, far fewer tokens than fetching all bodies. Switch to mode="full" only when you need the actual article texts (slower, much more tokens). Per-page extract failures are isolated. Disabled in cloud mode.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesSearch query.
limitNoNumber of results to extract (default 5, max 10).
max_charsNoTruncate each result body. Default depends on mode: ~1500 for abstract, 8000 for full.
modeNoExtraction depth per result. `abstract` (default) = cheap survey, ~1500 chars/result, ideal for relevance triage. `full` = whole body per result, slower and far more tokens; only when you actually need the article texts.abstract

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryNo
resultsNo
elapsed_msNo
metaNo
errorNo
Behavior4/5

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

Annotations already declare readOnlyHint=true, so the read-only nature is known. The description adds useful behavioral details: per-page extract failures are isolated, disabled in cloud mode, and the trade-offs between abstract and full modes. No contradiction with annotations.

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 (3-4 sentences) and front-loaded with the primary action. Every sentence adds value: main function, default behavior, mode switch guidance, failure handling, and cloud mode restriction. No waste.

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 that an output schema exists and schema coverage is 100%, the description is fairly complete. It explains the return format (SERP with abstracts), failure isolation, and mode behavior. Minor missing details about error handling or the exact output structure, but overall sufficient.

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 coverage is 100%, so baseline is 3. The description adds significant meaning beyond the schema: explains the purpose of 'mode' (abstract vs. full), default values for max_chars based on mode, and the limit range with defaults. This enriches the parameter understanding.

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 performs a 'one-shot Google search + parallel extract of the top results,' using a specific verb and resource. It distinguishes from siblings like 'search' (likely just returns links) and 'extract' (maybe single page) by mentioning parallel extraction and modes.

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 provides clear guidance on when to use each mode (abstract vs. full) based on token cost and speed. However, it does not explicitly differentiate this tool from its sibling 'search_parallel' or state when not to use it in favor of another tool.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/HarimxChoi/google-surf-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server