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thenavidm

ScrapeCreators MCP Server

by thenavidm

Pull Requests

github_pull_requests

Search public GitHub pull requests authored by a user via username, handle, or URL, with optional since/until date filters for created dates. Returns titles, repos, state, URLs, newest first.

Instructions

Searches public GitHub pull requests authored by a user using GitHub's public search index. Pass username, handle, or url. Optional since and until filters use YYYY-MM-DD created dates. Results include the PR title, repo, state, created_at, and url, sorted by newest created first. Potentially consumes paid API credits; requires confirm=true. Read-like POST requests do not publish to social platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sinceNoOnly return pull requests created on or after this date. Use YYYY-MM-DD.
untilNoOnly return pull requests created on or before this date. Use YYYY-MM-DD.
cursorNoCursor from the previous response. Defaults to 1.
handleYesGitHub username/handle of the user you want pull requests for
accountNoNamed private ScrapeCreators account; selects credentials, not a remote account ID.
confirmNoMust be true for the specific approved credit-consuming research call.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv2.0.0

TDQS

A3.9/5.0
Behavior4/5

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

Despite readOnlyHint=false, the description usefully discloses that this consumes paid API credits, requires confirm=true, and that read-like POSTs do not publish to social platforms — real context beyond the annotations. It also reveals result contents and sort order. It stops short of describing pagination/cursor behavior.

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 purpose, then filters, then result shape, then the credit/confirm caveat. Four tight sentences with no filler, though the credit/confirm sentence could be slightly more integrated.

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?

With no output schema, the description helpfully enumerates returned fields (title, repo, state, created_at, url) and sort order, and covers the credit/confirm requirement. Only cursor/pagination semantics are unaddressed, a minor gap for this tool.

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 baseline is 3. The description restates date format (YYYY-MM-DD) already documented in the schema and notes identity can be a username/handle/url, but adds little operational detail beyond what the schema already carries.

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?

States a specific verb and resource ('Searches public GitHub pull requests authored by a user'), plus scope ('public search index') and the input identity (username/handle/url). This is clearly distinguishable from siblings like github_activity, github_contributions, and github_repositories.

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 implies usage ('authored by a user', use since/until to filter), so an agent can infer the scenario. However, it gives no explicit when-to-use vs alternatives (e.g., github_activity or github_contributions for other GitHub signals) and no exclusions, leaving routing 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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