youtube-context-mcp

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.6.0
Disambiguation5/5
Each tool targets a distinct aspect of YouTube video interaction: building links, retrieving heatmap peaks, fetching transcripts, getting metadata, and listing available transcripts. There is no functional overlap.
Naming Consistency5/5
All tool names follow a consistent verb_noun snake_case pattern (build_, get_, list_), making the set predictable and easy to navigate.
Tool Count5/5
Five tools is well-scoped for a YouTube context server—enough to cover core operations without being overwhelming or incomplete.
Completeness4/5
The server covers key operations: metadata retrieval, transcript access, heatmap analysis, and link generation. Minor gaps like advanced search or comment retrieval are outside the stated purpose.
Average 4.6/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- Last stable release on June 11, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is passing
This repository is licensed under MIT License.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add glama.json to the root of your repository:
```
{
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}
```
Then . Browse examples.
Add related servers to improve discoverability.

How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It discloses key behaviors: does not transcribe audio, only returns existing captions, default language is ["en"], include_timestamps groups into ~15s blocks with [mm:ss] prefix, and translate_to performs translation. It does not mention error handling (e.g., missing video) or side effects, but for a read-only tool, the covered traits are sufficient.
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?
The description is well-structured with a concise opening sentence, a clarifying limitation, then parameter details in a list-like format, and finally return type. It is front-loaded. While every sentence is valuable, the length could be slightly reduced without losing clarity, but overall it is efficient.
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?
Given 4 parameters, no output schema, and no annotations, the description is fairly complete. It explains the return (plain text), all parameters, and key behavior. It could add more on error scenarios (e.g., no captions found returns empty string or error), but for typical use it is adequate.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description must fully explain each parameter. It does so: video (YouTube URL/ID format), languages (priority order, default ["en"]), include_timestamps (purpose and format), translate_to (optional ISO code). The description adds critical meaning beyond the schema's minimal title/type fields.
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 clearly states it fetches existing YouTube captions as text, explicitly distinguishes itself from audio transcription ("it does not transcribe audio"), and the verb "Fetch" plus resource "YouTube video's existing captions" is specific. This differentiates it from sibling tools like list_transcripts (which lists available captions) and get_video_metadata (metadata).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context: use this tool to get existing captions to answer questions about a video. It also clarifies a limitation (only works if captions exist). However, it does not explicitly state when not to use it or suggest alternative sibling tools (e.g., use list_transcripts to check availability first, or get_video_metadata for non-caption info).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully explains input formats and return value. Lacks mention of error handling or validation, but overall transparent.
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?
Well-structured with Args/Returns sections. Could be slightly more concise, but the example paragraph adds clarity.
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?
Complete for a simple tool: covers all inputs, return format, and use case. No output schema, but the return is clearly described.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Despite 0% schema description coverage, the description thoroughly explains both parameters: video accepts various URL forms or ID; start can be seconds or time strings. Provides examples.
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?
Clearly states the action (build a link) and the specific resource (YouTube video at a moment). Differentiates from sibling tools like get_transcript or get_video_metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit use case pairing with get_transcript to turn time stamps into clickable links. Does not explicitly mention when not to use, but the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden of behavioral disclosure. It explains that include_description is omitted by default to keep responses small, and it specifies valid input formats for the video parameter. It doesn't discuss rate limits, authentication, or error handling, but for a read-only metadata fetch, the transparency is adequate.
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 concise and well-structured, starting with the overall purpose, then providing usage guidance, and finally detailing arguments in a labeled format. Every sentence adds value without redundancy.
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?
Given the tool's simplicity (2 parameters, output schema exists), the description is complete. It covers what metadata is returned, the input format, and an optional flag. The presence of an output schema means return values need not be described further.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has no parameter descriptions (0% coverage), so the description must compensate entirely. It does so excellently: it explains the video parameter accepts various YouTube URL formats or a video ID, and the include_description parameter details its effect and rationale for omission.
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 clearly states the tool retrieves a YouTube video's metadata (title, channel, etc.) and distinguishes itself from retrieving the transcript, as indicated by 'without fetching its transcript.' The verb 'get' and resource 'video metadata' are specific and differentiate from sibling tools like get_transcript.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using this tool for metadata questions without fetching the transcript, providing clear context for when to use it. However, it does not explicitly state when not to use it or mention alternatives like get_most_replayed or build_video_link, which are siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description discloses behavioral traits: returns one image, tiles are non-readable, image dimensions, server dependency (ffmpeg), clamping of parameters, and potential duplicate tiles. Lacks explicit read-only statement but implied; no mention of auth or rate limits, but acceptable for this tool.
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?
The description is well-organized with clear sections (what, when, requirements, parameters). Every sentence adds value, but it is slightly lengthy. The embedded Args block is justified given the schema lacks descriptions.
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?
Given 5 parameters, no output schema, and no annotations, the description covers purpose, usage, parameter details, constraints, and common pitfalls (near-duplicate tiles). It could be more explicit about the output format (e.g., image dimensions), but overall it is sufficiently complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema has 0% description coverage, but the description's Args section adds extensive meaning: explains video accepts URL or ID, tiles and tile_width with clamping and defaults, start/end accept seconds or time strings with defaults. This compensates fully for the schema gap.
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?
Clearly states the tool returns a single tiled contact-sheet image with timestamp legend, distinguishing it from sibling tools like get_video_frame (single frame) and get_transcript (text). The verb 'get' and resource 'visual overview' are specific.
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?
Explicitly advises when to use (to get an overview), when not to (avoid binary-searching), and how to zoom in using start/end parameters with guidance to first use transcript or get_most_replayed. Also warns about near-duplicate tiles for short windows.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description fully discloses key behaviors: relative_intensity is relative within video, not comparable; is_opening flag indicates inflated interest; has_data may be False; profile curve shape description. This provides comprehensive transparency.
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?
The description is detailed but well-structured, with main purpose upfront. Every sentence adds value, though some redundancy could be trimmed. Overall, it is appropriately sized for the complexity.
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?
The description covers all important aspects: output fields, edge cases (no heatmap, inflated opening), and usage guidance. Given the presence of an output schema, it does not need to detail return format, making the description complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Although schema coverage is 0%, the description fully explains both parameters: video accepts various YouTube URL formats or video ID; top_n is clamped 1-20 with default 8 and the opening peak is additional. This compensates for the lack of schema descriptions.
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 clearly states the tool retrieves 'most replayed' moments from a YouTube video, specifying the resource and the specific data. It distinguishes from siblings like get_transcript and get_video_metadata by focusing on viewer-interest heatmap peaks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear use cases (e.g., finding best parts, jumping to good parts) and warns about the inflated opening peak and the possibility of missing data. It does not explicitly exclude alternative tools, but the use cases are distinct enough from siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden and discloses key behaviors: requires ffmpeg on server, captured frame is nearest keyframe (may be off by seconds), downscaled to max_width (clamped 64-1280, default 640), and mentions response size implications. This is thorough for a frame capture tool.
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 front-loaded with purpose, then usage, then technical details and parameters. It is thorough but slightly lengthy; every sentence adds value, so it earns a high score.
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?
Given no output schema, the description does not specify the returned image format (e.g., base64, URL) or error handling. However, it covers parameters well and provides usage context. For a simple tool, it is fairly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, but description fully compensates: 'video' explained as YouTube URL or ID; 'at' as seconds or mm:ss format; 'max_width' with range, default, and cost implication for vision models. Each parameter is clearly described beyond schema type.
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?
Description clearly states 'Capture a single still frame (screenshot) from a YouTube video at a moment and return it as an image'. It specifies the resource (YouTube video) and action, and indirectly distinguishes from sibling tools by mentioning pairing with get_most_replayed or get_transcript.
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?
Explicitly says 'Use this to see the video at a specific time' and provides examples (read a slide, caption, UI). Suggests pairing with get_most_replayed or get_transcript to pick moments, giving clear context for when to use this tool versus siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Although no annotations are provided, the description details what the tool reports (language, code, auto-generated, translatable) and notes that it also returns languages for translation. This goes beyond basic functionality, though it does not explicitly state read-only or side-effect-free behavior.
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?
Two short paragraphs with clear front-loading of purpose and usage. Every sentence adds value; no redundancy or fluff.
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?
Given a single parameter and output schema existence, the description covers all necessary aspects: what it does, when to use, parameter format, and output content. Complete for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description fully explains the single parameter 'video' with format guidance ('A YouTube URL or an 11-character video ID'). Since schema coverage is 0%, the description compensates completely, adding essential meaning.
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 clearly states the tool lists available transcripts for a YouTube video, with a specific verb 'List' and resource 'transcripts'. It also distinguishes itself from sibling tool get_transcript by indicating when to use this tool.
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?
Explicitly provides usage context: 'Use this when get_transcript can't find your requested language.' This tells the agent when to choose this tool over alternatives, meeting the highest standard.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![youtube-context-mcp MCP server](https://glama.ai/mcp/servers/realiti4/youtube-context-mcp/badges/card.svg)](https://glama.ai/mcp/servers/realiti4/youtube-context-mcp)

Score Badge

Copy to your README.md:

[![youtube-context-mcp MCP server](https://glama.ai/mcp/servers/realiti4/youtube-context-mcp/badges/score.svg)](https://glama.ai/mcp/servers/realiti4/youtube-context-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/realiti4/youtube-context-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server