YouTube transcript summarizer, AI-ranked video search, multi-video research reports, and channel growth audits via MCP. Four paid tools ($0.02/$0.05/$0.15/$0.25 USDC on Base via x402) plus free health_check and video_preview taster.
Multi-video AI research report on any YouTube topic.
Searches for the top videos on a topic, fetches their transcripts, and runs a comprehensive cross-video analysis through Muse Spark 1.3. Returns a research-grade report with synthesis, contrasting viewpoints, knowledge gaps, and recommendations.
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Base64 x402 payment payload (required — pass the X-PAYMENT value as an argument over MCP) | |
| num_videos | No | Number of videos to analyze (default 3, max 5) | |
| search_term | Yes | Topic to research across multiple videos |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotenceHint=true, so the safety profile is covered. The description adds useful behavioral context by explaining the internal workflow (searching, fetching transcripts, running analysis) and the nature of the returned report, going beyond what annotations provide. It does not mention the required x_payment, but that is documented in the schema.
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 compact and well-structured: the first sentence gives the core purpose, and the following sentences elaborate on the process and deliverables. Every sentence carries meaningful information, and there is no redundant or filler content.
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?
The description explains the output format in enough detail to compensate for the lack of an output schema, and it covers the overall process from search to analysis. It lacks explicit comparison to sibling tools and does not mention payment/cost, but the schema covers the x_payment parameter. Overall, it is sufficiently complete for a read-only research tool.
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 input schema provides complete descriptions for all three parameters, including defaults, max, and the required x_payment payload. Schema coverage is 100%, so the description does not need to add param-level detail. The description adds only marginal context about videos being top-ranked, which is not directly parameter-specific.
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 what the tool does: produces a multi-video AI research report on a topic. It details the pipeline (search, transcripts, cross-video analysis via Muse Spark 1.3) and explicitly names the report contents, which distinguishes it from sibling tools focused on single-video intelligence or channel growth.
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 implies when to use the tool (when you want a cross-video research report on a topic), but it does not explicitly state when to prefer alternatives like search_and_rank_videos or get_video_intelligence. No when-not-to-use conditions or exclusions are provided, leaving the guidance at an implied level.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract and AI-analyze a YouTube video transcript.
Fetches the full transcript using caption data, then runs it through Muse Spark 1.3 to produce a structured intelligence report: executive summary, key insights, notable quotes, topics covered, sentiment, and actionable takeaways.
| Name | Required | Description | Default |
|---|---|---|---|
| video_url | No | YouTube video URL or video ID (preferred) | |
| x_payment | No | Base64 x402 payment payload (required — pass the X-PAYMENT value as an argument over MCP) | |
| video_url_or_title | No | Alias for video_url (accepted for compatibility) |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 transparently explains the two-step process — fetching captions and running them through Muse Spark 1.3 — and lists the output sections. It does not disclose failure modes or the payment requirement, but the latter is clearly marked in the x_payment parameter description.
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 compact and front-loaded: the first sentence states the core action, the second explains the process and outputs. There is no filler or redundant restatement of the title.
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?
The description covers the main behavior and outputs, and an output schema exists so return-value details are not required. However, it omits usage guidance relative to the sibling tools and does not mention the required x_payment prerequisite outside the schema, leaving a small completeness gap for an agent selecting among multiple video-related tools.
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%, with all three parameters carrying descriptions. The tool description itself adds no parameter-level detail beyond naming video_url and the output process. Baseline 3 is appropriate since the schema already documents each parameter adequately.
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 opens with a specific verb-plus-resource statement: 'Extract and AI-analyze a YouTube video transcript.' It then enumerates the structured report contents, making it clear what the tool produces and distinguishing it from sibling tools like search_and_rank_videos or video_preview.
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 implies the tool is used when you need an AI-generated intelligence report from a YouTube video's transcript. However, it does not explicitly state when to prefer this tool over siblings like analyze_youtube_topic or grow_channel_report, nor does it mention any exclusions or alternative conditions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
YouTube channel growth audit: what this channel must do to grow.
Give a channel URL, @handle, or channel ID. Audits recent uploads (packaging grades, rewritten titles), validates niche demand against top-performing videos, and returns a 90-day action plan. Slow call (audits multiple videos) — allow up to 5 minutes.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | "html" (default, includes styled report) or "json" (structured data only) | html |
| x_payment | No | Base64 x402 payment payload (required — pass the X-PAYMENT value as an argument over MCP) | |
| channel_input | Yes | Channel URL (/@handle or /channel/UC...), @handle, or UC id |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses what the tool processes (recent uploads, packaging, titles), what it evaluates (niche demand vs. top-performing videos), and its output (90-day action plan). It also explicitly warns about slow execution time. It does not explicitly mention payment or read-only behavior, but these are at least partially covered by schema descriptions and the audit nature of the tool.
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 compact and well-organized: purpose first, then input expectations, process details, and a latency warning. Every sentence contributes new information with no fluff or repetition.
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 output schema is provided and all parameters are documented, the description completes the picture by stating the tool's purpose, input types, key processing steps, output type, and the important 'up to 5 minutes' latency caveat. An agent has enough context to select and invoke this tool correctly without further assumptions.
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 all three parameters are documented in the schema. The description repeats guidance for channel_input ('Give a channel URL, @handle, or channel ID') but adds no additional parameter-level meaning beyond the schema. This matches the baseline score for well-covered schema descriptions.
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 a specific verb and resource: 'YouTube channel growth audit' and enumerates concrete outputs (packaging grades, rewritten titles, niche demand validation, 90-day action plan). It is distinct from sibling tools like analyze_youtube_topic or search_and_rank_videos because it targets a full channel-level growth audit rather than a narrower analysis or search.
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 communicates when to use the tool: when a user wants a channel growth audit with actionable recommendations. The 'Slow call... allow up to 5 minutes' warning provides an important timing consideration. However, it does not explicitly name alternatives or state conditions when another sibling tool would be a better fit.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free service health check (no payment). Use to verify the server is live.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden and addresses a common concern by stating the check is free ('no payment'). 'Health check' plus 'verify the server is live' conveys a non-mutating status probe, and since there are no inputs, no permission or parameter side-effects need disclosure.
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?
Two short sentences deliver the key facts—free and purpose—with no fluff. The purpose is front-loaded and every word earns its place.
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?
For a no-input health check with an output schema, the description is complete: it states that it is free and specifies the verification purpose. The output schema covers return details, so nothing essential is missing.
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, so the baseline is 4; there is no parameter meaning for the description to add. The input schema already fully defines the empty input contract.
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 states a specific verb ('verify') and resource ('server is live') tied to a health check, and the service is clearly distinct from the analytics-focused sibling tools. Even without reading schemas, an agent can tell what this tool does.
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 phrase 'Use to verify the server is live' provides a clear, actionable when-to-use condition. It does not mention exclusions or alternatives, but no sibling tool overlaps with this health-check purpose, so this is a minor gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search YouTube and return AI-ranked results with relevance reasoning.
Uses the YouTube Data API to find videos, then Muse Spark AI ranks them by relevance, credibility, and content quality — returning a curated shortlist with reasoning for each recommendation.
| Name | Required | Description | Default |
|---|---|---|---|
| x_payment | No | Base64 x402 payment payload (required — pass the X-PAYMENT value as an argument over MCP) | |
| num_videos | No | Number of videos to retrieve before AI ranking (default 5, max 20) | |
| search_term | Yes | The topic or query to search for |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the burden, and it does well by disclosing the two-stage behavior: YouTube Data API search followed by Muse Spark AI ranking by relevance, credibility, and content quality. It also sets expectations for output with 'curated shortlist with reasoning.' It does not mention the x_payment prerequisite, though the schema already marks it required.
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 with no fluff. The core action is front-loaded in the first sentence, and the second sentence adds just enough mechanism detail without restating the schema.
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?
An output schema exists, so return values do not need explanation. The description covers purpose, methodology, ranking criteria, and output style. The only missing piece is explicit mention of the x_payment requirement, but that is clearly documented in the schema, so the tool is adequately specified for selection and invocation.
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 input schema has 100% coverage and describes search_term, num_videos, and x_payment. The description adds no parameter-level details beyond the schema, but the mention of relevance ranking indirectly reinforces that search_term is the core query. Baseline 3 is appropriate since the schema handles parameter semantics.
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 opens with a specific verb and resource: 'Search YouTube and return AI-ranked results with relevance reasoning.' It clearly states the tool's function, the ranking mechanism, and the output format, and it is distinct from siblings like video_preview or get_video_intelligence because it emphasizes AI ranking and a curated shortlist.
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 gives clear context: use this tool when you need to search YouTube and get AI-ranked, relevance-reasoned results. It does not explicitly name alternatives or exclusion conditions, but the 'AI-ranked' and 'curated shortlist' wording helps disambiguate from raw search or channel-analysis siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Raw YouTube transcript as a Markdown file (no AI analysis).
Returns timestamped, linkable Markdown with video metadata header — ready to save as a .md file. For AI summaries use get_video_intelligence.
| Name | Required | Description | Default |
|---|---|---|---|
| video_url | No | YouTube video URL or video ID | |
| x_payment | No | Base64 x402 payment payload (required — pass the X-PAYMENT value as an argument over MCP) |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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. It usefully states that the tool performs no AI analysis and returns timestamped, linkable Markdown with a video metadata header, which goes beyond the schema. It does not explicitly mention payment or auth side effects, though the x_payment parameter description covers that requirement.
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 concise and front-loaded with the core purpose, followed by the output format and a clear routing instruction. Every sentence earns its place and there is no redundant filler.
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 simple two-parameter schema, the presence of an output schema, and explicit sibling differentiation, the description is largely complete. It could be slightly more explicit about the required x_payment and the read-only nature, but those are partially covered by the schema context.
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 description coverage is 100%, so the schema already documents both video_url and x_payment. The tool description does not add additional parameter-level meaning, which is acceptable given the high schema coverage.
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 raw YouTube transcript as a Markdown file and explicitly notes 'no AI analysis.' It also names the differentiator by pointing to get_video_intelligence for AI summaries, making its purpose distinct from siblings.
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 says 'For AI summaries use get_video_intelligence,' giving the agent a clear when-not-to-use instruction. The phrase 'raw YouTube transcript' plus the contrast with AI analysis conveys the appropriate use case effectively.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Free YouTube video preview (no payment): title, channel, stats, and transcript excerpt.
Taster for get_video_intelligence. Shows value before asking for payment. Capped: transcript excerpt truncated to ~2000 chars, no AI analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| video_url | No | YouTube video URL or video ID |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses that the transcript is capped at ~2000 chars and that no AI analysis is included. Though no annotations are provided, the description covers the key behavioral boundary (free vs paid, truncated output). It doesn't mention error cases or authentication requirements, which with no annotations leaves some gaps.
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?
Compact and front-loaded with the core value proposition first. Each sentence earns its place: what it returns, when to use it, and its limitations. No redundancy or waste.
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?
Complete for a simple single-parameter preview tool with an output schema covering the return values. The absence of annotations is partially mitigated by the description's clear scoping. Could add what happens on invalid URLs, but the output schema covers the return format.
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 the schema already documents the video_url parameter. The description doesn't add extra detail about URL formats beyond the schema's 'YouTube video URL or video ID' explanation. Baseline 3 is appropriate since the schema does the heavy lifting.
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 provides a free YouTube video preview with title, channel, stats, and transcript excerpt. It distinguishes itself from the paid get_video_intelligence tool by explicitly positioning itself as a free taster.
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?
Explicitly positions this as a taster for get_video_intelligence and states the payment distinction. An agent can clearly understand when to use this free preview vs the alternative paid intelligence tool.
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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The HTTP ownership file has this structure:
{
"$schema": "https://glama.ai/mcp/schemas/connector.json",
"claim": "glama_claim_..."
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Each tool targets a distinct YouTube-related workflow: topic research, single-video intelligence, channel audit, search ranking, raw transcript, preview, and health check. A minor point of possible confusion exists between analyze_youtube_topic and search_and_rank_videos because both return curated video results, and video_preview overlaps lightly with get_video_intelligence, but the descriptions clarify the boundary.
All names use lowercase snake_case, but the pattern is mixed: analyze_youtube_topic, get_video_intelligence, and search_and_rank_videos are verb-first, while health_check, transcript_markdown, and video_preview are noun phrases. grow_channel_report is awkward as a verb phrase and would be clearer as get_channel_growth_report.
Seven tools is well-scoped for the server's purpose: research, video intelligence, channel growth, search ranking, transcript access, preview, and health check. Each tool has a clear role without redundancy or bloat.
The core workflows are covered: finding and ranking videos, analyzing single or multiple videos, pulling raw transcripts, and auditing a channel's growth opportunity. Minor gaps exist around more granular channel analytics or metadata-only lookups, but agents can work around them with the provided tools.