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Social Fetch

by io.github.social-freak-ltd

Generate web page markdown

web_markdown_generate
Read-only

Convert a web page URL into clean markdown.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesWeb page URL to fetch.
queryNoOptional query string used by the bm25 filter to rank relevant content.
filterNoMarkdown extraction filter. `fit`: strip boilerplate and extract the main readable content. `raw`: full unfiltered page markdown, no content pruning. `bm25`: rank and return only the content most relevant to `query`, using the BM25 keyword-relevance algorithm — requires `query` to be set.fit
waitForNoWait for a CSS selector before extraction. Must be prefixed with "css:" (e.g. css:main). JavaScript wait conditions are not supported.
cacheModeNoCache behavior. `enabled`: read from cache if present, else fetch and write to cache. `bypass`: always fetch fresh, ignoring and not updating the cache. `write_only`: always fetch fresh, but write the result to cache without reading from it first. Default: `enabled`.enabled
scanFullPageNoWhen true, scroll the page to load dynamically appended content (infinite scroll). Default false.

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, covering the safety profile. The description adds little beyond that, only 'clean markdown' suggesting some content filtering. It does not disclose aspects like timeout behavior, handling of unaccessible pages, or the fact that it may fetch external resources (though openWorldHint covers that). Since annotations provide the base, this is adequate but not enriched.

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?

The description is a single, direct sentence with no filler. It's front-loaded with the action and resource. Every word carries meaning. It avoids the tautology pitfall by stating what it does without merely restating the name.

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?

While the schema thoroughly documents parameters and the annotations cover safety, the description lacks guidance on selecting this tool among its web siblings and doesn't explain the output format (though it's implied as markdown). For a tool with six configurable options, the description alone is insufficient to fully contextualize its usage, but the schema fills most gaps. It's adequate but not comprehensive.

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 all six parameters (url, query, filter, waitFor, cacheMode, scanFullPage) are already well-documented in the schema. The tool description doesn't add any extra meaning to the parameters. Per the rubric, high coverage sets a baseline of 3, and the description adds no further value.

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?

The description 'Convert a web page URL into clean markdown' specifies a clear verb (convert), resource (web page URL), and output (clean markdown). It distinguishes this tool from siblings like web_html_generate (which outputs HTML) and web_screenshot_capture (which outputs images). The phrase 'clean markdown' also hints at the filtering behavior, adding specificity beyond the title.

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

Usage Guidelines2/5

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

The description provides no guidance on when to use this tool versus the many sibling web tools (web_ask_run, web_crawl_run, web_extract_run, etc.). It doesn't mention alternatives, exclusions, or typical use cases. The name and description imply it's for markdown conversion, but no explicit routing is provided, leaving the agent to infer.

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

B3.4/5.0
Disambiguation5/5

Each tool is clearly scoped to a specific platform and action (e.g., facebook_post_get vs instagram_post_get). Descriptions explicitly differentiate similar tools across platforms, and within-a-platform tools like tiktok_search_videos_list vs tiktok_search_hashtag_list have clear disambiguation notes.

Naming Consistency5/5

All 167 tools follow a strict `platform_resource_action` pattern (e.g., youtube_video_comments_list). No mixing of styles—snake_case throughout, with consistent verb ordering (get, list, search, etc.).

Tool Count2/5

The server has 167 tools, which is far beyond the typical well-scoped range of 3-15. While the broad multi-platform scope justifies many tools, this extreme number makes the tool surface overwhelming and difficult for an agent to navigate efficiently.

Completeness4/5

The tool set covers a wide range of platforms and operations including profile retrieval, post/video fetching, comments, search, transcripts, and ad library access. Minor gaps exist (e.g., no Facebook events or LinkedIn messaging), but the surface is comprehensive for a read-only data aggregation use case.

Resources