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Glama
starnikovoleg

serpens-mcp

Google search

google_search
Read-only

Run live Google searches to retrieve organic results, snippets, sitelinks, People Also Ask, and related searches for current facts and documentation lookups.

Instructions

Search Google and get organic results — title, link, snippet, source, position, plus sitelinks, People Also Ask and related searches when Google renders them. Use for current facts, documentation lookups, and anything past the training cutoff.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSearch query. Google operators work, e.g. "site:github.com fastify".
glNoCountry code, two lowercase letters. Default "us".
hlNoInterface language, e.g. "en" or "en-gb". Default "en".
tbsNoGoogle time/filter token, e.g. "qdr:d" for the past day.
pageNoResult page, 1-based. No upper bound.
formatNoOutput shape. "markdown" is compact prose for a model to read; "json" is the raw API response. Default "markdown".
autocorrectNoLet Google correct the spelling. Default true.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.1

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the description only needs to add behavioral nuance. It does this by specifying that results are organic, that features like sitelinks and People Also Ask only appear 'when Google renders them,' and that it is appropriate for post-training-cutoff facts, which usefully elaborates the open-world behavior.

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?

Two sentences contain a clear action, a precise list of return content, conditional behavior, and explicit use cases. No words are wasted, and the most important information is front-loaded in the first clause.

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 read-only search tool with a fully described schema, the description sufficiently explains what the agent gets and when it is appropriate to use it. There is no output schema, but the returned result types are enumerated; a minor gap is the lack of mention about failures, rate limits, or empty-result behavior.

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 description coverage is 100%, so the schema already documents all seven parameters and their defaults. The description does not add parameter-level detail, which is acceptable under the baseline; the parameter meanings are fully discoverable from 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 opens with a specific verb and resource: 'Search Google and get organic results.' It lists concrete returned fields (title, link, snippet, source, position, sitelinks, People Also Ask, related searches), which clearly differentiates it from the sibling google_ai_overview.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit use cases: 'current facts, documentation lookups, and anything past the training cutoff.' It does not name the sibling or provide exclusion criteria, but the use-case framing is clear enough for an agent to judge when to invoke it.

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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