YouTube Research MCP
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation4/5
The tools mostly target distinct actions: metadata retrieval, search, transcript extraction, pinpointing content, and autonomous research. The only potential confusion is between youtube_search (finding videos) and youtube_research (which also discovers videos), but the descriptions clarify that youtube_research is a higher-level aggregation tool that subsumes search and transcript extraction.
Naming Consistency3/5All tools share the 'youtube_' prefix, which is consistent, but the suffixes mix nouns (video, transcript, research) with verbs (search, find_in_video). This is not a strict verb_noun pattern like the calibration example. The inconsistency is minor but noticeable; a more uniform pattern like youtube_get_video, youtube_search_videos, etc., would improve coherence.
Tool Count5/5With 5 tools, the count is well within the ideal 3-15 range. Each tool serves a distinct purpose in the research workflow (discovery, metadata, transcript, semantic search, aggregation), and none feels redundant. The number is appropriate for the server's stated purpose.
Completeness4/5The tool surface covers the core research lifecycle: search, inspect (metadata), extract (transcript), locate (find_in_video), and synthesize (research). Minor gaps exist, such as no tool for channel-specific queries or playlist retrieval, but these are not essential for the primary research workflow. The inclusion of caption availability checks and language provenance shows attention to detail.
Average 4/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 13 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
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavioral traits on its own. It describes the internal process (discovery, concurrent transcript extraction, semantic search, clustering) but does not mention key constraints like read-only nature, potential duration (multi-step autonomous tool likely takes time), or any side effects. It is informative but incomplete regarding operational boundaries.
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 a single sentence that front-loads the core purpose ('Autonomous multi-video research tool') and then lists key capabilities efficiently. It is concise but slightly run-on, packing many clauses together. Still, it earns its place with no filler.
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?
The tool is complex (7 parameters, autonomous behavior) and has an output schema, so return values need not be described. However, the description omits practical context such as that the tool may be long-running or resource-intensive, and it does not mention any limitations or prerequisites. Given no annotations, the description carries the full burden and leaves gaps that an agent might need for correct usage.
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 input schema covers 100% of parameters with descriptions, so the baseline is 3. The description does not add any parameter-specific meaning beyond what the schema already provides; it only gives a high-level overview. No extra value is contributed here.
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 states a clear, specific purpose: an autonomous multi-video research tool that discovers videos, extracts transcripts, performs semantic search, and produces timestamped citations with clustering. This clearly distinguishes it from sibling tools like youtube_search or youtube_transcript, which handle individual operations. The verb 'research' and resource 'multi-video' make its scope unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for broad research across multiple videos, but it does not explicitly state when to use this tool versus the siblings (e.g., 'for a single video use youtube_video'). There are no when-not-to-use conditions or named alternatives. The context is clear enough to infer the intended use, but guidance is not explicit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. It adds useful context like 'without needing an API key' and describes the return shape, but it does not disclose potential limitations such as rate limits, result freshness, ranking behavior, or possible unreliability. This is acceptable but leaves room for more transparency.
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?
Two sentences with no filler. Key behavioral information is front-loaded, and the usage guidance is appended cleanly. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With a complete input schema and an output schema present, the description covers the essential context. It could be more complete by explicitly positioning itself against youtube_research, but an agent can still select and invoke it correctly based on the current wording and schema.
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 baseline is 3. The description adds little beyond the schema: it emphasizes querying and returning structured results but does not clarify parameter semantics or formats beyond what the input schema already documents.
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 action ('Search YouTube'), the target resource, and the specific output (structured list with IDs, titles, channels, durations, views, URLs). It also frames the tool's niche as discovery and candidate selection, which distinguishes it from siblings like youtube_transcript or youtube_video.
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 explicitly says to use it when discovering videos on a topic or finding candidates for research. It does not mention when not to use it or compare directly with alternatives such as youtube_research, but the guidance is clear enough for common use cases.
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 provided, the description carries the behavioral disclosure burden. It discloses the returned provenance flags (requested_language, actual_language, fallback_used) and makes an explicit behavioral guarantee: 'Never silently swaps languages unless fallback_language is specified.' This meaningfully informs the agent about how language selection behaves.
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 three concise sentences with no filler. The most important information, what the tool extracts and returns, is front-loaded, followed by a useful behavioral guarantee.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the core behavior, output provenance flags, and language fallback semantics. It is reasonably complete given the detailed input schema and an output schema; it does not cover every edge case like missing captions, but it provides enough for an agent to select and call the tool correctly.
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 baseline is 3. The description adds some value by explaining the fallback language behavior and provenance flags, but it does not substantially elaborate on translate_to or include_timestamps beyond what the schema already states.
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 opens with a specific verb and resource: 'Extract the spoken transcript of a YouTube video.' It also clarifies the output includes timestamped segments and language provenance, which immediately distinguishes this from sibling tools like youtube_search or youtube_video.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The purpose implies this tool is for retrieving a video's transcript, and the wording 'Extract the spoken transcript' gives reasonable context. However, it does not explicitly state when to prefer this over alternatives such as youtube_find_in_video, nor does it mention exclusions or conditions for selection.
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 the underlying search mechanism (Hybrid RRF with FastEmbed ONNX and BM25) and precisely what is returned: deep-link URLs, relevance scores, chapter context, and spoken quotes. This is transparent about the tool's operation and output. It doesn't mention any side effects or prerequisites, but for a read-only search tool, the disclosed behavior is sufficiently clear.
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 concise, with the core purpose front-loaded in the first sentence, followed by technical details and a usage recommendation. It wastes no words and is easy to scan. It could be slightly more structured (e.g., separating the technical method from the usage note), but it is efficient and appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
An output schema is present (indicated by 'Has output schema: true'), so the description doesn't need to detail return values, but it does list the key output elements (URLs, scores, chapter context, quotes) which helps agents understand what to expect. It lacks explicit mention of error conditions or prerequisites (e.g., transcript availability), but given the tool's simplicity and the schema coverage, it is reasonably complete for an agent to call it correctly.
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 has 100% description coverage, so the baseline is 3. The description adds minimal extra meaning beyond the schema: it rephrases 'query' as 'specific question, topic, or concept' and mentions output types, but it does not elaborate on parameter specifics like language or max_results behavior beyond what the schema already provides. Thus, it meets the baseline without adding substantial value.
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 tool's purpose: pinpoint exact sections and timestamps in a long video for a specific topic. It uses a specific verb (pinpoint), a specific resource (long video), and distinct output details (deep-link URLs, relevance scores, quotes). It also distinguishes itself from siblings like youtube_transcript by explicitly positioning itself as the preferred tool over full transcripts for long videos.
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 gives concrete usage guidance: it is 'PREFERRED over reading full transcripts for videos longer than 10 minutes.' This implies when to use it (long videos) and contrasts with the transcript tool. It doesn't explicitly mention alternatives like youtube_search or youtube_research, but the context is clear. A minor gap is not specifying when not to use it beyond the transcript context.
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?
No annotations are provided, so the description carries the behavioral burden. It communicates a read-only inspection operation through 'Retrieve' and 'inspect', and clarifies that it only determines transcript availability rather than returning transcript text. It does not discuss rate limits or error behavior, but these are less critical for a single-parameter read tool.
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?
Two sentences, front-loaded with the primary verb and resource, followed by a direct usage note. No filler or repetition of schema details.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter read-only tool with an output schema present, the description fully covers what it does and when to use it. The only minor gap is not naming the transcript alternative, but the phrasing 'determine if spoken transcripts are available' adequately signals that boundary.
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 already documents video_id with type and format ('11-character YouTube video ID or full YouTube URL') at 100% coverage. The description adds no parameter-specific meaning beyond that, so the baseline of 3 applies.
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?
States a specific action (retrieve) on a specific resource (metadata for a specific YouTube video) and enumerates the data categories returned (statistics, tags, chapters, caption availability). This clearly separates it from sibling tools like youtube_search and youtube_transcript, which address different tasks.
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?
Provides explicit use context: 'Use this tool to inspect... chapters and determine if spoken transcripts are available.' However, it does not mention alternatives or exclusion conditions, such as using youtube_transcript for actual transcript content.
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/Unknowmyt1M/youtube-research-mcp'
If you have feedback or need assistance with the MCP directory API, please join our Discord server