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Oxylabs Web API MCP Server

Oxylabs Web API MCP Server

by thevastas
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

List scrape endpoints

list_scrapers
Read-only

Discover available scrape endpoints or inspect one endpoint's accepted parameters before scraping. Lists what exists and returns the authoritative input schema for a chosen endpoint.

Instructions

List the scrape endpoints this API implements, or describe one of them.

Call this before assuming a dedicated scraper does or does not exist for a target, then call it again with the endpoint name to see what that endpoint accepts. That is the authoritative parameter list — more current than any documentation. Run the endpoint itself with scrape_target.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_scrapers"
  }
}

Input Schema

NameRequiredDescriptionDefault
endpointNoName a scrape endpoint to get its parameters and their types instead of the list. Omit it to list what exists.

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint=true, openWorldHint=true), so the bar is lower. The description still adds non-obvious context: the returned parameter list is 'authoritative' and 'more current than any documentation', which tells the agent to trust this call over cached knowledge — a real behavioral trait beyond the 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?

Front-loaded with purpose, then the two-step usage pattern, then the hand-off to scrape_target. Every sentence carries a distinct instruction and there is no filler.

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?

An output schema exists, so return-value detail is unnecessary. Given a single optional parameter, full schema coverage, and clear routing to the sibling that executes scrapes, the description supplies everything an agent needs to call this 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 description coverage is 100% and the single `endpoint` parameter is already documented in the schema with an example ('scrape/amazon/search') and default null. The description restates the omit-vs-supply behavior, which reinforces but does not extend the schema, so baseline 3 applies.

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?

Starts with a specific verb+resource ('List the scrape endpoints') and immediately adds the second mode ('or describe one of them'). It also names the sibling that performs the actual work ('run the endpoint itself with scrape_target'), so an agent can separate discovery from execution without opening any schema.

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

Usage Guidelines5/5

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

Explicitly prescribes the workflow: call it before assuming a target has a dedicated scraper, then call again with the endpoint name to learn its parameters. It names the alternative (scrape_target) and the condition that selects it, leaving nothing to inference.

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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