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Glama

Decodo

by io.github.pipeworx-io

Decodo Google Search

decodo_google_search
Read-onlyIdempotent

Google search results scraping via Decodo (formerly Smartproxy) — runs a Google search through rotating proxies and returns structured organic results (position, title, url, snippet) plus related searches when parsing succeeds. BYOK — _apiKey is your Decodo Web Scraping API "username:password" credentials. Example: decodo_google_search({ query: "best running shoes 2026", geo: "United States", _apiKey: "user:pass" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
geoNoLocation name to geotarget the search, e.g. "United States", "United Kingdom". Optional.
queryYesThe Google search query, e.g. "best running shoes 2026"
localeNoLanguage code for the Google interface, e.g. "en-US", "de-DE". Optional.
_apiKeyYesDecodo Web Scraping API credentials as "username:password" (from https://dashboard.decodo.com).
page_countNoNumber of result pages to retrieve (1-10). Default 1.

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-decodo-api-key",
+    "query": "best running shoes 2026"
+  },
+  {
+    "_apiKey": "your-decodo-api-key",
+    "geo": "United Kingdom",
+    "locale": "en-GB",
+    "page_count": 2,
+    "query": "machine learning frameworks"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Annotations (readOnlyHint, idempotentHint, etc.) are consistent. Description adds valuable behavioral details: uses rotating proxies, returns structured results (position, title, url, snippet), related searches on success, and BYOK credential format. No contradiction.

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 short sentences plus an example; every part adds value. Front-loaded with purpose. No redundancy.

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

Completeness5/5

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

No output schema, but description explains return structure (organic results fields and related searches). Covers failure case (parsing succeeds). For a scraping tool with 5 well-documented params, this is complete.

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

Parameters4/5

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

Schema has 100% coverage of parameters. The description reinforces key parameters via example (_apiKey and query) and explains _apiKey format. Adds context about output structure, but does not add extensive parameter-specific info beyond 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?

Clearly states it scrapes Google search results via Decodo, specifying the verb ('scraping'/'runs a Google search') and resource ('Google search'). Distinguishes from sibling tools like decodo_amazon_product and decodo_scrape.

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?

Provides clear context: use for Google search with rotating proxies, returns organic results. Includes an example call. Does not explicitly state when not to use, but sibling names imply alternatives. Good enough for typical use.

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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TDQS

A4.2/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions; however, the family of ask_pipeworx tools (beta, grounded) and deep_research could cause selection ambiguity despite clear documentation.

Naming Consistency3/5

Tool names lack a consistent pattern; they mix imperatives, descriptive nouns, and domain prefixes. While overall readable, the lack of uniformity makes it harder to predict naming conventions.

Tool Count4/5

34 tools is on the higher side but still within reasonable range given the broad scope (data queries, prediction markets, scraping, subscriptions, memory). Each tool appears purposeful, though some consolidation (e.g., ask_pipeworx variants) could reduce count.

Completeness4/5

The tool set covers a wide range of tasks from data querying to prediction market analysis and entity management. Minor gaps might exist (e.g., no direct social media data), but the overall coverage is extensive and sufficient for the platform's purpose.

Resources