Cloudglue MCP Server

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by cloudglue

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Server Quality Checklist

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Latest release: v0.3.1
Disambiguation5/5
Each tool targets a distinct video analysis operation: description, entity extraction, metadata, collections, video listing, summaries, semantic search (moments vs summaries), and segmentation (shots vs chapters). Descriptions clearly differentiate purposes.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern (describe_video, list_collections, search_video_moments, etc.), making it predictable for agents to infer tool functions.
Tool Count5/5
10 tools is well-scoped for a video analysis server, covering core operations without redundancy or excessive granularity.
Completeness4/5
Covers essential analysis workflows: metadata, description, entities, search, segmentation. Minor gaps like direct transcript retrieval or video upload are not critical for the stated purpose.
Average 4.4/5 across 10 of 10 tools scored. Lowest: 3.7/5.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 3 commits in the last 12 weeks
- Last stable release on April 1, 2026
- 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.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
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{
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  ]
}
```
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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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and openWorld hints. The description adds context about the API used (Cloudglue's search API), valid collections, and return format (structured results with relevance scores), which enhances understanding beyond annotations.
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 five sentences, front-loaded with purpose, and each sentence adds value (purpose, API, collections, return type, use case). No redundancy or unnecessary information.
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 description covers purpose, collections, and return type, but lacks details on result fields (e.g., video IDs, scores) and pagination. Given no output schema, the agent has limited information to parse results.
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 baseline is 3. The description adds general context but does not provide new parameter-level details beyond the schema's explanations of collection_id prefix removal and query examples.
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 it performs semantic search to find relevant videos within a collection, specifying the verb ('search') and resource ('videos'). It differentiates from siblings like list_videos and retrieve_summaries, but does not explicitly contrast with search_video_moments, a similar sibling.
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 discovering videos by topic, theme, or content similarity, but provides no explicit when-to-use or when-not-to-use guidance relative to sibling tools like search_video_moments.
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?
Annotations already indicate idempotentHint=true and destructiveHint=false. The description adds context about automatically checking for existing shot segmentation jobs before creating new ones, reinforcing idempotency. However, it does not disclose potential side effects, cost implications, or rate limits beyond what annotations provide, so the description adds only modest value.
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 at four sentences, each serving a clear purpose: stating the main function, mentioning auto-checking, describing output, and listing URL constraints. It is front-loaded with the core action and avoids unnecessary details.
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 single parameter with full schema coverage, the description adequately covers usage context: supported URL types, output summary, and idempotent behavior. It lacks details on error handling or output structure, but the absence of an output schema is mitigated by the brief mention of 'timestamps and metadata'.
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?
With 100% schema description coverage, the input schema already provides exhaustive detail about the 'url' parameter, including examples and format options. The tool description reiterates some of this information (supported URLs, YouTube exclusion) but does not add new semantic meaning beyond what the schema offers.
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 purpose: 'Segment videos into camera shots with intelligent cost optimization.' It specifies the action (segment), the resource (videos into camera shots), and the output (timestamps and metadata). This distinguishes it from the sibling tool 'segment_video_chapters,' which segments by chapters instead of shots.
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 explicit guidance on when to use the tool, including supported URL formats and a clear exclusion: 'YouTube URLs are not supported for segmentation.' It also mentions automatic checking for existing jobs, implying idempotent usage. However, it does not directly compare with alternatives like segment_video_chapters or describe scenarios where one segmentation type is preferred over another.
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?
Annotations provide idempotentHint=true, and the description reinforces this by stating the tool automatically checks for existing extractions. It also discloses error behavior (returns error if not found) and mentions cost optimization. However, it does not describe output structure or comprehensive error handling.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is clear and front-loaded but somewhat verbose, with redundancy on pagination (schema already covers it). The 'intelligent cost optimization' phrase is vague and adds little value. About 150 words, could be trimmed.
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?
Covers the two modes and pagination well, but lacks any description of the output format or structure. Since no output schema exists, the description should at least hint at what the agent will receive, e.g., entity types or JSON structure.
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?
All four parameters have schema descriptions (100% coverage), and the description adds meaningful context: URL examples and reference, prompt examples, collection_id mode explanation, and page segment details. This goes beyond the schema.
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 extracts structured data and entities from videos, with two distinct modes (fetch existing vs extract new) and supported URL types. It explicitly says 'Use this for individual video analysis,' distinguishing it from sibling tools like describe_video or search_video_moments.
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 explains when to use each mode based on whether collection_id or prompt is provided, and mentions pagination. However, it lacks explicit guidance on when not to use this tool or comparison with sibling tools beyond a brief final sentence.
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?
Annotations already indicate read-only and non-destructive. The description adds valuable details about pagination (25 per page, page/total_pages fields) and response content, which exceeds what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is efficient with no redundant sentences. It front-loads the main purpose and filters, then explains pagination. A minor trade-off for completeness, but very well structured.
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 no output schema, the description adequately explains response fields (page, total_pages, duration, filename, IDs) and usage pattern. It covers filtering and pagination, making it complete for the tool's complexity.
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?
Schema has 100% coverage, but the description adds context beyond field descriptions, such as pagination mechanics (page 0 = first 25) and the presence of total_pages in response. This extra guidance aids correct usage.
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 it is for browsing and searching video metadata, and distinguishes itself from sibling tools like describe_video and get_video_metadata by focusing on listing multiple videos with filtering.
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?
Provides clear context for when to use (explore, find by date, collection), and hints at pagination workflow. However, it does not explicitly state when not to use or name alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses automatic check for existing descriptions before creation, aligning with idempotentHint=true. Adds behavioral details on pagination and cost optimization beyond annotations, with no contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Front-loaded with purpose, then details. Slightly verbose but each sentence adds necessary context, covering URL types, pagination, and collection workflow. Could be trimmed without losing clarity.
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?
Covers pagination and collection reuse well, but lacks details on the output format (structure of descriptions) and mentions 'different analysis levels' without elaboration. Without an output schema, the description should provide more on return values.
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?
Schema covers 100% of parameters with descriptions. The tool description adds value by explaining the combined use of start_time_seconds and page for precise navigation, and the purpose of collection_id, going beyond the schema's inline descriptions.
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 'Gets comprehensive video descriptions' with specific verb+resource. Distinguishes from sibling tools like get_video_metadata and extract_video_entities by focusing on descriptions with pagination and cost optimization.
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?
Specifies 'Use this for individual video analysis' and explains when to use collection_id to save time/cost. Lacks explicit alternatives or when-not-to-use guidance, but context is clear from sibling tool names.
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?
Annotations already declare readOnlyHint true, destructiveHint false, idempotentHint true, openWorldHint true. The description adds context about the kind of data returned (technical metadata, processing status) without contradicting annotations. It could mention pagination or error behavior but is still 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 two concise sentences with no extraneous information. It front-loads the core purpose and includes usage guidance efficiently.
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 has a single parameter, no output schema, but strong annotations, the description adequately explains the tool's scope and differentiates it. It lists example metadata fields, providing a clear picture of output without needing full schema. A minor gap is that it doesn't explicitly state that the output is a JSON object, but the example fields compensate.
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%, with the file_id parameter detailed in the schema itself. The main description does not add additional parameter meaning beyond what the schema provides, meeting the baseline for high schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves technical metadata about a Cloudglue video file, listing specific attributes (duration, resolution, etc.). It distinguishes from sibling tools like describe_video by specifying it is for technical specs rather than content analysis.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says when to use the tool ('Use this when you need video specifications...') and contrasts it with content-focused tools, citing describe_video as an alternative.
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?
The description adds context beyond annotations (e.g., API used, return format with timestamps and metadata). Annotations already declare readOnlyHint=true and idempotentHint=true, so the description's behavioral disclosure is adequate and consistent.
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 with three sentences: one on what it does, one on how it works, and one on when to use it. No wasted words; front-loaded with the core purpose.
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 is complete for a read-only search tool: it explains inputs, semantic search capability, and output structure (timestamps, metadata). Without an output schema, it sufficiently covers return values, though more detail on response fields could improve completeness.
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?
Schema description coverage is 100% with detailed descriptions for both parameters. The description adds value by explaining the prefix convention for collection_id and providing example queries, enhancing understanding beyond the schema alone.
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 it performs 'AI-powered semantic search to find specific video segments,' using specific verb+resource, and the examples distinguish it from siblings like search_video_summaries by focusing on precise moments across speech, text, and visual content.
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 explicit use cases like 'needle-in-haystack spoken and visual content, specific discussions, or thematic analysis,' but does not explicitly mention when to avoid using it or alternatives among siblings.
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?
Annotations already declare readOnlyHint, destructiveHint, idempotentHint, and openWorldHint. The description adds pagination behavior (25 per page, page/total_pages in response) and relationships to other tools. It does not contradict annotations but could detail the metadata fields more.
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 well-structured, front-loaded with the main purpose, then sequentially covers usage order, type-specific guidance, and pagination details. Every sentence adds value without redundancy.
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 no output schema, the description partially fills the gap by mentioning page and total_pages fields, but does not fully specify the structure of each collection item beyond IDs, counts, and types. Still reasonably complete for a list tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 100% schema coverage baseline at 3, the description adds significant context: explains pagination semantics for page parameter (page 0 = first 25, etc.) and clarifies collection_type values with usage directives (media-descriptions -> describe_video, entities -> extract_video_entities).
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 discovers available video collections and their basic metadata, and distinguishes it from siblings by advising to use this first. It explicitly mentions the collection IDs needed for other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit when-to-use guidance ('Use this first'), links to alternative tools for different collection types (describe_video, extract_video_entities), and explains pagination with the page parameter.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Discloses automatic job checking (supports idempotency hint), cost optimization, and return of timestamps/descriptions. No contradiction with annotations.
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?
Concise and front-loaded with main purpose. Every sentence adds value without redundancy.
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?
Covers all key aspects: purpose, supported URL types, duplicate prevention, and return values. Adequate given no output schema.
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?
Schema covers 100% of parameters with detailed descriptions. The tool description adds context about cost optimization and duplicate checking, providing extra value beyond schema.
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 the tool segments videos into chapters with cost optimization. Distinguishes from sibling tools like segment_video_camera_shots by specifying chapter segmentation.
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?
Provides context on when to use (for chapter segmentation) and mentions duplicate detection. However, does not explicitly state when not to use or compare with alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it as read-only and idempotent, and the description adds pagination details (25 per page, page parameter, page and total_pages in response). This fully discloses behavior beyond annotations without contradiction.
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 well-structured and front-loaded: first sentence states purpose, then usage guidelines, alternatives, and pagination details. Every sentence adds distinct value; no fluff.
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?
Covers all key aspects: purpose, when to use, alternatives, pagination behavior, date filtering, and response fields (page, total_pages). No output schema, but the description sufficiently informs about return structure.
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 input schema has 100% description coverage, so the baseline is 3. The description adds value by explaining the 'first step' strategy for collection analysis and reinforcing pagination mechanics, going slightly beyond the schema.
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 purpose: bulk retrieving video summaries and titles from a collection. It distinguishes itself from siblings like describe_video (single videos) and search_video_summaries (targeted content discovery), leaving no ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit when-to-use guidance: 'first step when analyzing a collection' and 'more efficient than retrieving full descriptions'. Also specifies when not to use it (for single videos or targeted search) and names alternative tools (describe_video, search_video_summaries, search_video_moments).
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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