Multilingual YouTube → Knowledge Pack engine. Paste a video URL and get a structured pack — summary, key ideas, glossary, quiz, transcript with timestamps — in Spanish, Portuguese, German, or English. Anonymous endpoint plus OAuth-gated tools for library search, RAG Q&A on a single pack, and Anki export.
Glama MCP Gateway
Connect through Glama MCP Gateway for full control over tool access and complete visibility into every call.
Full call logging
Every tool call is logged with complete inputs and outputs, so you can debug issues and audit what your agents are doing.
Tool access control
Enable or disable individual tools per connector, so you decide what your agents can and cannot do.
Managed credentials
Glama handles OAuth flows, token storage, and automatic rotation, so credentials never expire on your clients.
Usage analytics
See which tools your agents call, how often, and when, so you can understand usage patterns and catch anomalies.
Average 4.6/5 across 1 of 1 tools scored.
With only one tool, there is no possibility of confusion between tools. The single tool has a clearly defined purpose.
The single tool name 'vozclara_generate_pack' follows a consistent verb_noun pattern (generate_pack), which is clear and standard.
A single tool for generating knowledge packs is borderline appropriate. While it may cover the core functionality, the server feels thin and could benefit from additional tools for managing packs or supporting different workflows.
The toolset is severely incomplete for a service that generates and presumably manages knowledge packs. Missing CRUD operations (e.g., listing, updating, deleting packs) and other potential features like retrieving transcripts or supporting multiple formats beyond Obsidian.
vozclara_generate_packGenerate a Knowledge Pack from a YouTube videoAInspectUse this when the user asks for a summary, key ideas, or study material from a YouTube video. Returns a structured Knowledge Pack: title, short summary, key ideas, and a link to the full pack on vozclara.app. Pass format="obsidian" to get vault-ready Markdown with YAML frontmatter the user can save to their second brain.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | Public YouTube video URL (youtube.com/watch?v=… or youtu.be/… short form). | |
| depth | No | Depth of the generated pack. `short` = one-paragraph summary. `standard` = summary + 3-5 key ideas. `deep` = summary + key ideas + glossary + quiz. | standard |
| format | No | Output format. `standard` = plain prose for chat. `obsidian` = Markdown with YAML frontmatter (source, videoId, languages, tags) ready to write straight into an Obsidian vault or any Markdown knowledge base. Use `obsidian` when the user asks to save the pack to their vault / second brain / notes. | standard |
| language | Yes | Output language for the Knowledge Pack: es (Spanish), pt (Portuguese), de (German), en (English). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility. It discloses the tool returns a structured pack with specific components and a link. It does not mention rate limits, processing time, or authentication needs, but for a read-only generation tool, the transparency is adequate. A slight gap is the lack of statement about non-destructiveness.
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 description is two sentences long, with the first sentence front-loading the purpose and trigger, and the second detailing the return value and format guidance. Every sentence serves a clear purpose with zero redundancy.
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 absence of output schema and annotations, the description adequately covers purpose, when to use, key parameter behavior, and return structure. It could include a note about the full link being accessible on vozclara.app, but overall it is sufficiently complete for an agent to invoke the tool correctly.
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?
Schema coverage is 100%, so baseline is 3. The description adds value by explaining when to use `obsidian` format and the effect of different `depth` values (e.g., `short` = one-paragraph summary). This provides context beyond the schema's simple enum descriptions, warranting a 4.
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 generates a Knowledge Pack from a YouTube video, listing the returned fields (title, summary, key ideas, link). It unambiguously defines the verb and resource, and implicitly distinguishes from any potential sibling by focusing on YouTube videos.
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?
The description explicitly tells when to use: 'when the user asks for a summary, key ideas, or study material from a YouTube video.' It also provides specific advice for the `format` parameter, guiding agents to use `obsidian` for vault-saving requests. No sibling tools exist, so exclusion guidance is unnecessary.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Claim this connector by publishing a /.well-known/glama.json file on your server's domain with the following structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"maintainers": [{ "email": "your-email@example.com" }]
}The email address must match the email associated with your Glama account. Once published, Glama will automatically detect and verify the file within a few minutes.
Control your server's listing on Glama, including description and metadata
Access analytics and receive server usage reports
Get monitoring and health status updates for your server
Feature your server to boost visibility and reach more users
For users:
Full audit trail – every tool call is logged with inputs and outputs for compliance and debugging
Granular tool control – enable or disable individual tools per connector to limit what your AI agents can do
Centralized credential management – store and rotate API keys and OAuth tokens in one place
Change alerts – get notified when a connector changes its schema, adds or removes tools, or updates tool definitions, so nothing breaks silently
For server owners:
Proven adoption – public usage metrics on your listing show real-world traction and build trust with prospective users
Tool-level analytics – see which tools are being used most, helping you prioritize development and documentation
Direct user feedback – users can report issues and suggest improvements through the listing, giving you a channel you would not have otherwise
The connector status is unhealthy when Glama is unable to successfully connect to the server. This can happen for several reasons:
The server is experiencing an outage
The URL of the server is wrong
Credentials required to access the server are missing or invalid
If you are the owner of this MCP connector and would like to make modifications to the listing, including providing test credentials for accessing the server, please contact support@glama.ai.
No comments yet. Be the first to start the discussion!
