YouTube Researcher MCP Server

Server Quality Checklist

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Latest release: v1.1.0
Disambiguation3/5
The tools are mostly distinct, but youtube_search_niche and youtube_analyse_niche overlap in purpose—both search a niche, differing only in analysis depth. The description for search_niche is also confusingly worded, mentioning video IDs from a previous search, which blurs its role.
Naming Consistency4/5
All tools use a consistent 'youtube_' prefix with snake_case, and most follow a verb_noun pattern (e.g., get_video_details, analyse_niche). However, 'quota_status' lacks a verb, deviating slightly from the otherwise coherent naming scheme.
Tool Count5/5
With 6 tools, the server is well-scoped for a YouTube research purpose. Each tool covers a necessary function—search, analysis, video details, channel details, thumbnails, and quota—without excess redundancy.
Completeness4/5
The toolset covers core research workflows: search/analyze niches, retrieve video/channel metadata, download thumbnails, and monitor API usage. Minor gaps exist, such as no direct comment retrieval or transcript access, but the main research lifecycle is well-covered.
Average 3.9/5 across 6 of 6 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
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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?
No annotations are provided, so the description carries the full burden. The verb 'Fetch' implies a read-only operation, and the listed fields give a sense of the response, but there is no mention of quota usage, error behavior, or rate limits. Adding some context about these aspects would improve the score.
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 two sentences long, front-loaded with the core action and resource, and the second sentence adds valuable usage context without redundancy. No filler words or unnecessary explanations.
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?
The description explains what the tool returns (subscriber count, total views, etc.) and why it's useful, covering enough to operate effectively. It does not explicitly mention the input parameter or error scenarios, but the schema covers the input, and the tool is simple enough that this description is sufficient. Slightly more detail on output format or edge cases would push it to a 5.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the parameter 'channelIds' is already well-described in the schema (array, max 50). The description itself does not add parameter-specific details but does reinforce the overall purpose. Baseline 3 is appropriate given the schema's completeness.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the tool as fetching channel metadata and lists specific fields (subscriber count, total views, video count, creation date). It distinguishes from siblings by focusing on 'channels' rather than videos or niches, but does not explicitly compare or contrast with alternatives.
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 a clear use case (understanding competitive landscape, assessing channel sizes) but does not mention when not to use it or explicitly name alternative tools. This counts as clear context without exclusions.
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 full burden. It discloses the return fields (title, description, tags, etc.) and batching efficiency (50 per API call), which adds value. However, it omits details like rate limits, authentication requirements, or error handling, which could impact invocation. This is adequate but not rich.
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 sentences, front-loaded with the core purpose, followed by a concise list of return fields and batching behavior. Every word earns its place, with no redundancy or filler.
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?
For a simple single-parameter tool with no output schema, the description provides a comprehensive list of return fields and batching behavior, giving the agent a clear picture of what to expect. Minor gaps exist, such as definitions of 'engagement rate' and 'view velocity' or error scenarios, but for a read-only metadata fetch, the description is largely sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% with a single parameter 'videoIds' clearly described as an array of YouTube video IDs (max 50). The description's mention of 'Batches efficiently (50 per API call)' reiterates the schema's max constraint, adding marginal semantic value. Baseline 3 is appropriate given high schema coverage.
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 'Fetch full metadata and calculated engagement metrics for one or more video IDs' – a specific verb and resource. This distinguishes it from sibling tools like youtube_search_niche, youtube_get_channel_details, and youtube_get_thumbnails by focusing on video ID-based metadata retrieval.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you have video IDs and want metadata, but it does not explicitly mention when to avoid this tool or name alternatives. The presence of sibling tools like youtube_get_channel_details and youtube_search_niche suggests different use cases, but the description does not reference them, leaving the agent to infer boundaries.
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?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It states what it returns but does not clarify whether the tool makes an API call itself, how the estimate is calculated, or any side effects, leaving a gap for a status tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is three short sentences, front-loading the purpose in the first sentence and providing contextual detail about API limits and typical costs without unnecessary verbosity.
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 the tool's simplicity (no params, no output schema), the description provides sufficient context about its purpose and API limits. It could be slightly more explicit about the expected return format, but this is a minor gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and the schema is empty. The baseline for zero parameters is 4, and the description does not need to elaborate on parameter semantics.
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's function with a specific verb ('Returns') and resource ('estimated API quota consumption'), and it distinguishes the tool from siblings like youtube_search_niche or youtube_analyse_niche by focusing solely on quota status.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for monitoring quota before running analyses (e.g., 'A typical full niche analysis costs ~103 units'), but it does not explicitly say when to use this tool versus alternatives or provide exclusions.
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 must carry behavioral disclosure. It states the output type (video IDs, basic metadata) and sort behavior, and explicitly disclaims metric calculations. However, it omits limitations like quota usage, pagination, or authentication requirements, which are relevant in YouTube API tools. The added context about 'raw results' is useful but incomplete.
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 sentences, cleanly front-loaded with the action and result, followed by usage guidance. No redundant wording or filler. Every sentence contributes value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool has 5 parameters and no output schema, so the description should specify return value structure more explicitly. 'Basic metadata' is vague—does it include title, channel, publish date? Also no mention of quota or pagination. The presence of youtube_quota_status as a sibling suggests quota is a concern, which should be noted. Overall, it's minimally complete but leaves gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema already covers all parameters with descriptions (100% coverage), so the description adds little beyond referencing the sort order. It doesn't clarify parameter details further, but the schema is sufficient. Baseline 3 is appropriate.
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 searches YouTube for videos in a niche or category and returns video IDs plus basic metadata sorted by order. This differentiates it from siblings like youtube_get_video_details, youtube_analyse_niche, and youtube_get_thumbnails by focusing on raw search results without metric analysis.
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?
It provides explicit usage context: 'Use this for targeted searches when you already have video IDs from a previous search or want raw search results without metric calculations.' This signals when to prefer this tool over analysis-heavy siblings, though it doesn't name an alternative directly. The phrase about having video IDs is slightly confusing but the overall guidance is helpful.
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 full burden. It discloses that it filters out Shorts (<60s) and active live streams automatically—an important behavioral trait. It also enumerates the computed metrics (median views, engagement rates, top tags, etc.), giving a clear picture of what the tool does beyond simply 'analysis.' It stops short of explaining potential rate-limit or quota behavior, but for a read-only data aggregation tool, transparency is strong.
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 front-loaded with the core purpose. It packs substantial detail into two sentences without redundancy. Every sentence adds value—first states the primary role, second enumerates metrics and automatic filters. No filler.
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?
With no output schema, the description compensates by listing the aggregate statistics returned (median views, engagement rates, top tags, duration distribution, publishing day patterns, channel size breakdown). It also clarifies filtering behavior. Minor gaps: it does not specify the exact response structure or how results are sorted, but for a high-level analysis tool, the description gives enough context for an agent to anticipate outputs.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so each parameter is already described. The tool description adds some contextual meaning (e.g., that minViews is applied after fetch, and that Shorts are filtered automatically), but it does not provide new semantic details for parameters beyond the schema. Baseline 3 is appropriate.
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 a specific verb+resource: 'High-level niche analysis' for YouTube research. It distinguishes from siblings by highlighting that it searches, fetches metadata, computes engagement metrics, and returns aggregate statistics—something none of the sibling tool names suggest. The phrase 'primary entry point' further clarifies its unique role.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It identifies itself as 'the primary entry point for YouTube research,' which implies it should be used first for broad niche analysis. However, it does not explicitly mention when to use sibling tools like youtube_get_video_details or youtube_search_niche as alternatives, nor does it provide exclusion criteria. Some context is present, but explicit guidance is lacking.
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 full burden of behavioral disclosure. It adds valuable details: the fallback strategy ('Tries maxresdefault first, falls back to hqdefault') and the cost implication ('No API quota cost'). While it does not cover error handling or file overwrite behavior, the disclosed traits are sufficient for this simple tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise: three sentences that front-load the primary action and then add relevant details (use case, fallback, quota) without waste. Every sentence earns its place.
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?
For a tool with two parameters and no output schema, the description is complete. It explains what the tool does, why it might be used, the fallback behavior, and the cost implication. No important gaps remain given the tool's simplicity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so both parameters are well-documented in the input schema. The description does not add extra parameter semantics beyond what the schema already provides, aligning with the baseline of 3 for high schema coverage.
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 uses a specific verb ('Download') and resource ('thumbnail images for video IDs to a local directory'), clearly distinguishing it from sibling tools that search, get details, or analyse niches. It also explains the intended use case (Claude vision analysis), reinforcing its unique purpose.
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 for when to use this tool (e.g., when needing thumbnails for vision analysis) and implicitly differentiates from sibling tools that do not download thumbnails. It lacks explicit exclusions or named alternatives, but the context is unambiguous.
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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