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ScrapeCreators MCP Server

ScrapeCreators MCP Server

by thenavidm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Trending Repositories

github_trending_repositories

Scrape GitHub's public Trending page to return ranked repositories with URLs, descriptions, languages, stars, forks, and contributors. Filter by daily/weekly/monthly range and language.

Instructions

Scrapes GitHub's public Trending repositories page. Returns ranked repositories with public URLs, descriptions, language, star/fork counts, stars for the selected range, and built-by users when GitHub shows them. Use language for paths like JavaScript or Python, since for daily/weekly/monthly, and spoken_language_code for GitHub's spoken language filter. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

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": "github_trending_repositories"
  }
}

Input Schema

NameRequiredDescriptionDefault
sinceNoTrending range: daily, weekly, or monthly. Defaults to daily.
accountNoNamed private ScrapeCreators account; selects credentials, not a remote account ID.
confirmNoMust be true for the specific approved credit-consuming research call.
languageNoOptional coding language, e.g. javascript, python, or go.
spoken_language_codeNoOptional spoken language code filter, e.g. en.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.8/5.0
Behavior4/5

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

Annotations declare readOnlyHint=false and destructiveHint=false, but the description adds genuinely useful context beyond them: it consumes paid API credits, requires confirm=true, and clarifies that the read-like POST does not publish to social platforms. It also enumerates returned fields, which the absent output schema cannot.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core action and return contents, then parameter hints, then the credit/confirm warning. Every sentence carries information, though the social-platform disclaimer is slightly awkwardly worded for the audience.

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?

Covers the important gaps for a zero-required-param scraper with no output schema: it lists the returned fields, discloses credit consumption and the confirm requirement, and explains the read-like POST behavior. An agent has everything needed to call 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%, so the schema already documents all five parameters including the since enum and defaults. The description's restatement of language/since/spoken_language_code usage adds marginal value but no syntax or format detail beyond the schema; baseline 3 applies.

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: 'Scrapes GitHub's public Trending repositories page'. The name and description make the target obvious, though the description never explicitly contrasts itself with the close sibling github_trending_developers.

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?

Most of the guidance is parameter-level ('use language for..., since for..., spoken_language_code for...') rather than when-to-use vs alternatives. There is no statement of when this tool is preferable to github_repositories or github_trending_developers, so usage is only implied.

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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