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Github Get Repo Stars Since

github_get_repo_stars_since

Read-only

Identifies repositories owned by a user that gained the most stars since a specified date, defaulting to 30 days ago. Answers prompts like 'Which repos gained the most stars recently?'

Instructions

Return the repos owned by username that received the most new stars since a given date. since accepts YYYY-MM-DD or ISO 8601, defaulting to 30 days ago. Answers prompts like 'which repos gained the most stars in the last 30 days'. Counts come from the weekly star history, so they resolve to whole UTC days rather than to exact star timestamps. Cost is one request for each page of the repo listing, then roughly one request per repo for every 30 weeks of the window, which makes a 30-day window a single request per repo however popular that repo is. truncated is True when the account has more public repos than the listing could read, or when a repo's history ran longer than the walk could read, so the answer may miss some.

Workflow and conventions: github_get_skill('user-activity').

Input Schema

TableJSON Schema

Name	Required	Description	Default
`since`	No
`top_n`	No
`username`	Yes
`max_repos`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
`repos`	Yes
`since`	Yes
`username`	Yes
`truncated`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Addedv42.0.0Sep 15, 2026

TDQS

A4.1/5.0

Behavior5/5

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

Beyond readOnly/openWorld annotations, it discloses the weekly-history UTC-day approximation, per-page/per-repo cost, and truncated semantics. No contradiction with annotations.

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?

The purpose and since format are front-loaded, and the cost/truncation sentences add needed context without filler. It is long but dense; could be trimmed slightly but still earns its length.

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

Completeness3/5

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

Good coverage of behavior, cost, and truncation with an output schema present, but the undocumented `top_n` and `max_repos` parameters create a real gap. An agent cannot fully understand the result-limit and repo-scan-limit semantics from the description.

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

Parameters2/5

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

Schema description coverage is 0%, so the description must carry parameter meaning; it only explains `since` (format/default). `top_n`, `max_repos`, and `username` are left to inference, which is a significant gap.

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: 'Return the repos owned by username that received the most new stars since a given date.' It also clarifies the ranking and differentiates from general repo listing/search siblings by focusing on star growth since a date.

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 concrete prompts like 'which repos gained the most stars in the last 30 days' and explains cost/truncation tradeoffs, signaling when it is appropriate. It does not explicitly compare against alternatives, but the use-case framing is enough to route an agent.

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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