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LinkDigest — Xiaohongshu, Douyin, TikTok, YouTube to text

Server Details

Xiaohongshu, Douyin, TikTok, YouTube, X links to text: transcript, on-screen text, images described

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Status
Healthy
Last Tested
Transport
Streamable HTTP
URL

Available Tools

1 tool
digest_urlAInspect

Turn a social media URL into LLM-ready context. Works on Xiaohongshu, Douyin, TikTok, YouTube, X and ordinary web pages. Returns the post's transcript, on-screen text, image descriptions, caption and metadata — the things you cannot get by fetching the URL yourself, because these posts are video or images behind tokenised share links. Use this whenever you are given a social media link. Bilibili, Instagram and Facebook are not supported. A long video may not finish in one call: if the result names a job id, call this tool again with that job_id (and no url) to collect it.

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesThe post URL, including any share tokens.
formatNomarkdown (default, best for reading) or json (structured).
job_idNoCollect a digest already running. Pass the job id from a previous call instead of url. Use this rather than re-sending the url, which would start the work again.

TDQS

A4.7/5.0
Behavior4/5

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

With no annotations, the description carries the behavioral disclosure burden. It openly explains why the tool is needed (tokenised share links, video/image content), what it returns, and the asynchronous job-id behavior for long videos. It does not mention error behavior for unsupported or invalid URLs, but the disclosed details are meaningful and non-obvious.

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 tightly written and each sentence earns its place: core purpose, the rationale, the trigger, exclusions, and async continuation. It is front-loaded with the most important information and contains no redundant filler.

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?

Despite lacking an output schema, the description specifies what the tool returns, which platforms are supported, which are not, and how to handle incomplete long-video jobs. This is sufficient for an agent to select and invoke the tool correctly in most scenarios.

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

Parameters4/5

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

The schema already documents all three parameters with 100% coverage, so the baseline is 3. The description adds value by explaining why job_id exists — long videos may not finish in one call and re-sending the url would start the work again. This connects the parameter to real-world behavior beyond the schema text.

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 names a specific verb ('Turn a social media URL into LLM-ready context') and a clear resource, then enumerates supported platforms and the exact content returned. It distinguishes itself from simply fetching the URL by explaining that the content is not accessible that way. This makes the tool's purpose unmistakable.

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

Usage Guidelines5/5

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

The description gives an explicit trigger: 'Use this whenever you are given a social media link.' It also names unsupported platforms (Bilibili, Instagram, Facebook) so the agent knows when not to use it, and provides a polling instruction for long videos. With no sibling tools, this is as complete as usage guidance can be.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool update
    • First observeddigest_url

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TDQS

A4.7/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of selecting between overlapping tools. The single digest_url operation has a clearly defined input (URL or job_id) and output (transcript/metadata).

Naming Consistency5/5

The sole tool uses a clean verb_noun pattern (digest_url), which is descriptive and consistent. With only one tool, there are no naming inconsistencies to penalize.

Tool Count4/5

One tool is below the typical 3-15 range, but the server's scope is intentionally narrow: converting social media URLs to text. The single tool fully covers this purpose, so the count feels slightly sparse but not insufficient.

Completeness5/5

The tool covers all advertised platforms and handles both synchronous results and asynchronous job polling. Unsupported platforms are explicitly documented, so there are no hidden gaps for the stated domain.

Resources