JSON matrix of data types (metadata, insights, transcript, frames, comments) per platform — YouTube (+Shorts), TikTok, Instagram Reels, Pinterest, Reddit. Call before framefetch_extract to confirm support. No input.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, and the description adds that the tool takes no input and provides a capability matrix. It discloses the scope of the returned data (data types and platforms) without unnecessary detail, consistent with the read-only annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The entire description is three short sentences, front-loaded with the core function. No filler words—every part adds value (data types, platforms, usage hint, no-input confirmation).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool is a simple information retrieval with no input and a clearly described output, the description fully covers its purpose and usage context. The absence of an output schema is compensated by the explicit enumeration of what the matrix contains.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, and the description confirms 'No input.' This is redundant with the empty schema but reinforces the fact; since there are no parameters to explain, the baseline of 4 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool returns a JSON matrix of data types per platform, enumerating both data types (metadata, insights, transcript, frames, comments) and platforms (YouTube, TikTok, Instagram Reels, Pinterest, Reddit). It explicitly directs usage before framefetch_extract, distinguishing it from sibling tools like framefetch_extract, framefetch_search, and framefetch_account.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
It gives explicit guidance to call this tool before framefetch_extract to confirm support, providing direct usage context. This also implies the tool serves as a prerequisite check, making the alternative (framefetch_extract) and the timing clear.
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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Each tool maps to a clearly distinct function: balance checking, single-URL extraction, platform capability lookup, and YouTube search. The descriptions explicitly separate them, especially framing framefetch_account as read-only and framefetch_extract as the only paid operation. There is no real risk of selecting the wrong tool for a task.
All tools share the framefetch_ prefix and use consistent lowercase snake_case, giving the set a coherent namespace. However, the suffixes mix nouns like account and platform_capabilities with verbs like extract and search, so the naming is not as uniform as a strict verb_noun convention.
Four tools is well-scoped for a social-video extraction API: account, capabilities, search, and extraction each earn their place without overlap. The set is neither too thin nor bloated for its apparent purpose.
The tool surface covers the core workflow: check platform support, search for a video, extract desired data, and monitor account balance. There are no obvious dead ends or missing operations for the stated video-extraction domain.