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DeepSRT

DeepSRT MCP Server

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by DeepSRT
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TypeScript
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Server Quality Checklist

67%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one retrieves a summary, the other retrieves a transcript with timestamps. There is no overlap in functionality, making it easy for an agent to select the correct tool based on the need for either a concise overview or detailed textual content.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern (get_summary, get_transcript), using the same verb 'get' and descriptive nouns. This uniformity makes the tool set predictable and easy to understand.

    Tool Count2/5

    With only two tools, the server feels thin for a domain like YouTube video processing. While the tools cover basic retrieval, the scope is limited, lacking operations such as search, analysis, or management of video content, which could be expected from a more comprehensive server.

    Completeness2/5

    The tool set is severely incomplete for a YouTube-focused server. It only provides retrieval functions (summary and transcript), missing essential operations like video search, metadata fetching, comment handling, or any CRUD capabilities, leaving significant gaps in coverage for typical agent workflows.

  • Average 3/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No issues in the last 6 months
    • No commit activity data available
    • No stable releases found
    • 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.

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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

  • 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. It states what the tool does but doesn't mention any behavioral traits such as rate limits, authentication needs, error handling, or what the summary output looks like (e.g., format, length). This leaves significant gaps for a tool that likely interacts with external APIs.

    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 a single, efficient sentence that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to parse quickly.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness2/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the lack of annotations and output schema, the description is incomplete. It doesn't address key contextual aspects like the summary format, potential errors, or how it differs from the sibling tool. For a tool with external dependencies (YouTube API), more information on behavior and constraints is needed.

    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 the schema already documents all parameters (videoId, lang, mode) with descriptions and defaults. The description adds no additional meaning beyond what the schema provides, such as explaining what 'narrative' vs 'bullet' modes entail or how the lang parameter affects the summary.

    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 states the action ('Get summary') and resource ('for a YouTube video'), making the purpose immediately understandable. It distinguishes from the sibling tool 'get_transcript' by focusing on summaries rather than transcripts, though it doesn't explicitly mention this distinction.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    No guidance is provided on when to use this tool versus alternatives like 'get_transcript'. The description lacks context about prerequisites, limitations, or scenarios where this tool is preferred, leaving the agent with no usage direction beyond the basic purpose.

    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 the full burden of behavioral disclosure. It mentions 'with timestamps,' which adds some context about the output format, but fails to address critical aspects like rate limits, authentication needs, error handling, or whether the operation is read-only or has side effects.

    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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence contributes directly to understanding the tool's function, making it highly concise and well-structured.

    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?

    Given the tool's moderate complexity (2 parameters, no annotations, no output schema), the description is minimally adequate. It covers the basic purpose and output feature (timestamps) but lacks details on behavioral traits, usage guidelines, and output structure, leaving gaps for the agent to navigate.

    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 has 100% description coverage, clearly documenting both parameters. The description adds no additional parameter semantics beyond what the schema provides, such as examples or constraints. With high schema coverage, the baseline score of 3 is appropriate.

    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 states the tool's purpose: 'Get transcript for a YouTube video with timestamps.' It specifies the verb ('Get'), resource ('transcript'), and key feature ('with timestamps'), but doesn't explicitly differentiate from the sibling tool 'get_summary' beyond the resource type.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines2/5

    Does 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 like 'get_summary.' It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage based solely on the tool name and purpose.

    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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