YouTube MCP Server
Server Quality Checklist
Latest release: v1.0.0
- Disambiguation5/5
The two tools have clearly distinct purposes: playPlaylist is for playing videos in the browser, while searchVideos is for searching and returning video metadata. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.
Naming Consistency2/5The naming is inconsistent: playPlaylist uses camelCase, while searchVideos uses a verb_noun pattern. This mix of conventions lacks a predictable pattern, which could confuse agents expecting uniformity across tools.
Tool Count2/5With only 2 tools, the server feels thin for a YouTube domain, which typically involves operations like listing playlists, managing subscriptions, or uploading videos. The current set is insufficient for comprehensive YouTube interactions, suggesting a significant under-scoping.
Completeness2/5The tool surface is severely incomplete for a YouTube server. It lacks basic CRUD operations such as creating playlists, managing subscriptions, or handling user accounts. The existing tools cover only playback and search, leaving major gaps that will likely cause agent failures in broader tasks.
Average 3.3/5 across 2 of 2 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
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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.jsonto 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
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While it mentions what the tool returns, it doesn't cover important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or pagination behavior. The description is minimal and lacks the depth needed for a tool with no annotation support.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is appropriately concise with two clear sentences. The first sentence states the core functionality, and the second sentence specifies what information is returned. There's no wasted language or unnecessary elaboration. However, it could be slightly more structured by separating purpose from return values more explicitly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given that there are no annotations and no output schema, the description is insufficiently complete. For a search tool with 2 parameters, the description should provide more context about the search behavior, result format, limitations, and how it differs from the sibling 'playPlaylist' tool. The current description leaves too many behavioral questions unanswered.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema description coverage is 100%, with both parameters well-documented in the schema itself. The description doesn't add any meaningful parameter information beyond what's already in the schema - it mentions 'by query' which is already covered by the schema's query parameter description. This meets the baseline expectation when schema coverage is complete.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search for YouTube videos by query.' It specifies the verb (search) and resource (YouTube videos), and mentions the return fields (video IDs, titles, channels, descriptions). However, it doesn't explicitly differentiate from the sibling tool 'playPlaylist', which appears to serve a different function (playing playlists vs. searching videos).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention the sibling tool 'playPlaylist' or any other search-related tools that might exist. There's no context about when this search is appropriate or when other methods should be used instead.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. While it states the tool plays videos in the browser, it doesn't mention important behavioral aspects like whether this opens a new tab/window, requires browser permissions, has rate limits, or what happens if multiple instances are invoked. The description is insufficient for a mutation tool with zero annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is perfectly concise with two sentences that each earn their place. The first sentence states the core purpose, and the second explains the parameter options. There's zero wasted language and it's front-loaded with the essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 3 parameters, 100% schema coverage, but no annotations or output schema, the description provides adequate basic context about what the tool does and parameter options. However, as a mutation tool (playing videos implies side effects), it should disclose more about behavioral expectations and potential constraints given the lack of annotations.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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 three parameters thoroughly. The description adds marginal value by mentioning the alternative between videoIds and query parameters, but doesn't provide additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('Play YouTube videos in the browser') and resource ('YouTube videos'), distinguishing it from the sibling tool 'searchVideos' which presumably only searches without playing. It explicitly mentions both direct video ID input and search query functionality.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear context about when to use each parameter ('Provide video IDs directly or a search query'), but doesn't explicitly state when to choose this tool over the sibling 'searchVideos' or mention any prerequisites or exclusions. The guidance is helpful but lacks sibling differentiation.
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:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
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/HITENDRAPAL3/youtubeMCP'
If you have feedback or need assistance with the MCP directory API, please join our Discord server