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Glama

Social Fetch

by io.github.social-freak-ltd

Get TikTok video transcript

tiktok_video_transcript_get
Read-only

Get the spoken transcript for a TikTok video by URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
urlYesLink to the TikTok video whose transcript should be returned.
contextYesDescribe the user's underlying goal in one sentence — not the tool you are calling.
languageNoOptional two-letter language code to request a transcript in a specific language when available.
llm_modelYesThe exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
useAiFallbackNoWhen true, uses Social Fetch's AI fallback when a transcript is not otherwise available. Adds 10 credits on completed lookups (11 total with the base lookup).
conversation_idNoEcho the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed5 schema fields changed
    • removedInput schema / additionalProperties
      Removed value: -false
    • addedInput schema / properties / context
      Added value: +{
+  "description": "Describe the user's underlying goal in one sentence — not the tool you are calling.",
+  "type": "string"
+}
    • addedInput schema / properties / conversation_id
      Added value: +{
+  "description": "Echo the conversation_id from the server's previous response. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it.",
+  "type": "string"
+}
    • addedInput schema / properties / llm_model
      Added value: +{
+  "description": "The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. \"claude-opus-4-8\", \"gpt-5.2\"). Used for analytics only. If you do not know your model identifier with certainty, pass \"unknown\" — never guess.",
+  "type": "string"
+}
    • changedInput schema / required
      Previous value: -[
-  "url"
-]New value: +[
+  "url",
+  "context",
+  "llm_model"
+]
  2. Changed1 schema field changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
  3. Changed1 schema field changed
    • addedInput schema / additionalProperties
      Added value: +false
  4. Changed1 schema field changed
    • changedInput schema / properties / useAiFallback / anyOf
      Previous value: -[
-  {
-    "type": "string"
-  },
-  {
-    "type": "boolean"
-  }
-]New value: +[
+  {
+    "type": "boolean"
+  },
+  {
+    "enum": [
+      "0",
+      "1",
+      "true",
+      "false"
+    ],
+    "type": "string"
+  }
+]
  5. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true and openWorldHint=true, which already communicate safety. The description adds the 'spoken transcript' qualifier, clarifying the output is audio-derived rather than captions. The useAiFallback parameter description adds behavior context (credit cost, fallback behavior) that compensates for the lack of output schema. 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?

Description is a single concise sentence that conveys the core purpose without padding. It's appropriately sized for a simple retrieval tool, though it could have added a sentence about when to use it vs. alternatives without becoming bloated.

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 simple read-only transcript retrieval with 100% schema coverage and readOnlyHint=true, the description plus schema covers the essentials. The fallback parameter description documents credit costs. No output schema exists but the name and description make the return type (transcript text) obvious. Slight gap: no mention of language availability or failure behavior, but these are minor for this tool's simplicity.

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 schema already documents all six parameters. The description's 'by URL' phrase marginally reinforces the url parameter but adds no new semantics beyond the schema. With high schema coverage, baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description states a specific verb ('Get'), resource ('spoken transcript'), and input ('TikTok video by URL'). It is clear and distinguishable from siblings like tiktok_video_get and tiktok_video_download_get, though it doesn't explicitly name a sibling alternative like facebook_post_transcript_get or youtube_video_transcript_get which have similar purposes.

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?

Clear that the tool is for retrieving transcripts from TikTok videos, but doesn't explicitly state when to use this vs alternatives (e.g., tiktok_video_get for metadata, tiktok_video_download_get for downloading). No exclusion conditions or when-not-to-use guidance. Implied usage is clear from the name and description, but no explicit routing to alternatives.

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