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github_repositories

List a user's repos with sort/direction/type — opaque Link cursor (~0.4/repo). Costs ~12 credits (0.4/result). Empty results and failures are never charged. Pass cache=true for a free 24h cache hit (default always fresh).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNocreated | updated | pushed | full_name (default updated). Not stars — GitHub's user-repos API has no stars sort. Echoed as data.sort.
typeNoowner (default) | member | all — affiliation filter. Echoed as data.type.
cacheNoSet true to serve from the 24h response cache (0 credits on hit). Default false — always fetch fresh.
limitNoMax items to return (default 30, max 100). Billed per result.
cursorNoOpaque cursor from a previous nextCursor (GitHub Link page=). Not a bare page number.
usernameYesGitHub username or profile URL, e.g. torvalds.
directionNoasc or desc (default desc). Echoed as data.direction.

TDQS

A4.1/5.0
Behavior5/5

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

With no annotations present, the description carries the full burden and delivers: cost model ('~12 credits (0.4/result)'), billing policy ('Empty results and failures are never charged'), cache semantics ('cache=true for a free 24h cache hit (default always fresh)'), and opaque-cursor pagination. This far exceeds typical transparency for a read tool.

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?

Purpose is front-loaded in the first sentence, followed by cost, billing policy, and cache option — every sentence earns its place. A minor flaw: the per-item rate is stated twice ('~0.4/repo' and '0.4/result'), a small redundancy in an otherwise dense description.

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?

For a 7-parameter tool with no annotations and no output schema, the description covers the unusual operational aspects (cost, empty/failure billing, cache, cursor) while the schema covers all parameter semantics. The remaining gap is the response shape, partially mitigated by schema hints like 'from a previous nextCursor' and 'Echoed as data.sort'.

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 schema itself is unusually rich (defaults, option lists, the 'Not stars' caveat, 'Not a bare page number' cursor warning, 'Echoed as data.*'). The tool description only references sort/direction/type and cache=true at a high level, adding no incremental parameter meaning beyond the baseline.

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 ('List') and a specific resource ('a user's repos'), with qualifying dimensions 'sort/direction/type'. The plural resource distinctly separates it from close siblings like github_repository (single repo) and github_trending_repositories (not user-scoped), so an agent can distinguish it without opening schemas.

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 gives clear operational context — caching, costs, cursor pagination — but never addresses when to pick this over alternatives. Among ~150 siblings, github_repository (singular) is an obvious alternative for one repo's details, yet no when/when-not or alternative routing is given; usage is only implied by the name and 'List a user's repos'.

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

B3.2/5.0
Disambiguation3/5

The platform-prefix convention keeps most of the 178 tools clearly separated, but several clusters are genuinely ambiguous: tiktok_live_info is explicitly described as 'Identical to TikTok Live', instagram_basic_profile and instagram_channel_details both return profile stats, and facebook_profile_posts overlaps with facebook_profile_reels. The generic 'Summarizer' descriptions for facebook_summarize, instagram_summarize, and tiktok_summarize provide no disambiguating detail at all.

Naming Consistency4/5

The dominant snake_case platform_resource_suffix pattern is followed remarkably consistently across 178 tools (e.g. youtube_channel_videos, tiktok_search_users, reddit_subreddit_posts). Minor deviations exist: the same creator resource is called 'channel' in some tools (tiktok_channel_details, instagram_channel_posts) but 'profile' or 'user' in others (facebook_profile_posts, twitch_user_videos, linnkme_profile); link-in-bio tools mostly use _page but linkme uses _profile; and the video_summarize/video_transcript pair lacks a platform prefix.

Tool Count2/5

At 178 tools this is far beyond what any agent can efficiently navigate in a single flat namespace, and even individual platform subsets exceed reasonable bounds (TikTok alone has ~34 tools, YouTube ~25). The sheer breadth of the multi-platform scope partially justifies the count, but the server would be far more usable split into per-platform servers.

Completeness4/5

The read-only data surface is impressively thorough: nearly every platform has profile + content + search + comments coverage, and TikTok, YouTube, Instagram, and Facebook are covered end-to-end including shops, ads, transcripts, and summaries. Notable gaps are minor: Twitter has no keyword search tool, LinkedIn lacks comments, and Reddit has no user-profile endpoint, but none of these create dead ends for the server's core data-retrieval purpose.