mcp-video-vision

by danusiojr

Overview Schema Related Servers Score Discussions

TypeScript

Local

Server Quality Checklist

Profile completionA complete profile improves this server's visibility in search results.

Latest release: v0.1.0
Disambiguation4/5
Most tools have clearly distinct purposes (e.g., transcribe, video_info, generate_xml). However, there is some overlap: decupar and detect_scenes both detect scenes, and catalog_brolls also extracts representative frames, which could cause confusion. The descriptions help differentiate but ambiguity remains for these tools.
Naming Consistency2/5
Tool names mix English and Portuguese (catalog_brolls, decurpar vs. detect_scenes, extract_frames). Naming patterns are inconsistent: some are verb_noun (detect_scenes, extract_frames), others are noun_verb? (catalog_brolls) or single verbs (decupar). This inconsistency makes it harder to predict tool functions from names alone.
Tool Count5/5
8 tools is an appropriate number for a video analysis and editing server. Each tool covers a distinct part of the workflow (metadata, scene detection, frame extraction, transcription, XML generation) without being overwhelming or too sparse.
Completeness4/5
The tool set covers the core video analysis pipeline: metadata extraction, scene detection, frame extraction, audio transcription, and XML export for editing. Minor gaps exist: no tool for searching within transcriptions or for directly combining scenes into edit decisions, but these are manageable for an agent.
Average 3.5/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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.json to 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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided. The description does not disclose side effects, permissions, output format, or any behavioral traits beyond the basic action. Minimal transparency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two short sentences, front-loaded with the verb 'Extracts', no wasted words. Efficient.
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?
For a tool with 8 parameters and no output schema or annotations, the description is too brief. It does not explain return values, constraints like max_frames, or typical usage patterns.
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 covers 50% of parameters with descriptions; the description adds value by recommending auto_fps usage, but does not describe other parameters.
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 states the tool extracts frames at a fixed FPS interval, which is clear. It implicitly distinguishes from sibling 'frame_at' which likely extracts a single frame. However, it could be more explicit about the time range extraction.
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 on when to use this tool versus alternatives like 'frame_at' or 'detect_scenes'. Only mentions auto_fps option but no context for decision-making.
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 must disclose behavioral traits. It mentions the tool detects scenes and extracts frames, but does not state if it is read-only, destructive, or requires specific permissions. Lacks details on side effects or limitations.
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 concise (one sentence) and front-loads the purpose. However, given the complexity of the tool (8 parameters), it is too brief and lacks necessary structural details like parameter explanations or output format.
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?
The description does not explain the return value format of the navigable log, and there is no output schema. It also fails to contextualize the tool among siblings or provide enough information for the agent to correctly invoke it with appropriate parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 13% (1 of 8 parameters documented). The description does not explain any parameters beyond the high-level purpose. For a tool with many parameters, the description should add meaning, but it fails to do so.
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: it detects scenes, extracts one representative frame per shot, and returns a navigable content log. This distinguishes it from siblings like detect_scenes (only scene detection) or extract_frames (only frame extraction).
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 quick content overview without scrubbing, but does not explicitly state when to use this tool versus alternatives (e.g., detect_scenes, extract_frames). No exclusions or prerequisites are mentioned.
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 bears full responsibility for behavioral disclosure. It states that it extracts a single frame from a specific timestamp, but does not mention side effects (e.g., it is read-only), how invalid timestamps are handled, what the output is (file path, binary?), or any performance considerations. The behavioral transparency is minimal.
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 extremely concise with just two sentences. The first sentence states the core action, and the second provides a practical usage example. Every word is essential, and there is no redundant information.
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?
Despite being a simple tool, the description lacks important context: there is no output schema, so the return value is unclear; error handling or constraints (e.g., video format support) are not mentioned; and the parameter 'resolution' default value (1456) is unexplained. Given the low parameter coverage and no behavioral details, completeness is insufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema has 4 parameters with only 25% description coverage (only 'timestamp' has a description 'HH:MM:SS'). The description itself adds no parameter meaning beyond what the schema provides. For example, 'video_path' lacks any description, and 'resolution' and 'format' are not elaborated. The description does not compensate for the low 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 'Extrai um único frame de um timestamp exato' (extracts a single frame from an exact timestamp), distinguishing it from sibling tools like 'extract_frames' (plural) which likely extracts multiple frames. The example 'útil para o que tem em 1:30?' provides a concrete use case, making the purpose precise and actionable.
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 gives a usage example ('útil para o que tem em 1:30?'), which implies when to use it (inspecting a specific timestamp), but it does not explicitly state when not to use it or mention alternatives like 'extract_frames' for batch extraction. The guidance is implicit and lacks exclusion criteria.
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?
With no annotations, the description carries the full burden. It mentions the detection method and output format, but lacks details on side effects, prerequisites (ffmpeg), error handling, or behavior with edge cases like invalid paths. Adequate but not thorough.
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 consists of two concise sentences. It front-loads the core purpose and method, followed by output and use case. No wasted words.
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 complexity (4 parameters, no output schema, no annotations), the description is incomplete. It omits parameter explanations, return format, error scenarios, and prerequisites. The essential behavior is conveyed, but significant gaps remain for effective agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 25% (only 'threshold' has schema description). The tool description adds zero parameter information beyond the schema. For a low-coverage tool, this is insufficient; it fails to clarify unclear parameters like 'start_time' or 'end_time'.
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 detects scene cuts using ffmpeg and returns timestamps with confidence. It also notes it's ideal for decoupage (1 frame per shot). However, it does not explicitly differentiate from sibling tool 'decupar', which may have similar functionality.
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 usage context by stating it's ideal for decoupage, implying a specific use case. However, it does not give explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives among siblings.
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 full burden. It discloses file creation and return of XML but omits important traits like overwrite behavior, permission needs, or the max clip limit of 200 evident 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.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is highly concise: two sentences plus an import instruction. Every sentence adds value (generation, output, return, import), with no redundancy or fluff.
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?
For a tool generating XML from clips, the description lacks details on file overwrite, supported codecs, error handling, and the 200-clip limit from schema. No output schema, but the import instruction is helpful. Adequate but with 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?
Schema description coverage is moderate (4 of 6 parameters have descriptions). The description adds no extra meaning beyond the schema; it mentions 'editing decisions' referencing the clips parameter but does not explain width/height or fps defaults 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?
The description clearly states it generates a Premiere/FCP XML file from a list of editing decisions, specifying the output file saving and import instruction. It differentiates from sibling tools like decupar or catalog_brolls by focusing on XML generation.
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 editing decisions are available but does not explicitly state when to use vs alternatives, such as decupar for creating the decision list. No when-not-to-use or alternative guidance is provided.
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 provided, so the description must carry the burden. It explains the tool reads and analyzes clips (non-destructive) and returns a catalog, but does not mention auth needs, rate limits, or any side effects. Adequate but not fully transparent.
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 that front-load the core functionality and provide a usage hint. Every word earns its place with zero redundancy.
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?
With 4 parameters and no output schema, the description should explain the return structure and parameter effects. It only mentions 'catálogo completo com frames representativos' without details on format or how threshold/resolution affect output. Incomplete for moderate complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 50% (video_paths and max_shots_per_clip have descriptions). The description adds no parameter-specific information beyond the schema defaults for threshold and resolution, failing to compensate for the coverage gap.
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 visually analyzes a list of b-roll clips, performs decoupage (shot detection/segmentation), and returns a catalog with representative frames. It also suggests usage before match_edit, distinguishing it from siblings like decupar or detect_scenes.
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?
Explicitly says 'Use antes de match_edit' to allow Claude to see available visual content. Does not mention when not to use or alternatives, but provides clear context for a specific workflow step.
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 provided, so description carries full burden. It discloses use of local Whisper, return format with timestamps, and available models. However, it does not mention file size limits, processing time, error handling, or input validation.
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 purpose, no wasted words. Every sentence adds value.
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?
Description explains return format (full text with timestamps) and model options, sufficient for a simple transcription tool without output schema. Missing details on handling large files or errors, but adequate for typical use.
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 parameter descriptions. Description adds minimal extra meaning beyond schema (e.g., model default, usage context). Baseline 3 applies.
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?
Description clearly states it transcribes audio from video/audio files using local Whisper, returns full text with per-segment timestamps, and mentions its use in editing workflows. Distinct from sibling tools which focus on other video editing tasks.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides context for use in aligning narration with b-rolls, but does not explicitly state when not to use or mention alternatives. Context is clear but lacks exclusions.
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?
Without annotations, the description fully carries the behavioral burden. It explicitly states the tool reads metadata without processing frames, implying read-only and non-destructive behavior. It could add more details (e.g., file existence checks) but is sufficient for a simple metadata reader.
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 efficiently deliver the purpose and usage context without any extraneous information. Every sentence is valuable.
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 simple metadata-reading tool with one parameter and no output schema, the description is complete. It lists the metadata fields and positions the tool relative to its siblings, providing all necessary context.
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?
There is only one parameter with schema description coverage at 100%. The description does not add extra meaning beyond the schema's 'Caminho absoluto do arquivo de vídeo.' Baseline score 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 specifies the action (lê metadata) and the resource (vídeo), listing specific metadata fields (duração, fps, resolução, codec, tamanho) and distinguishing itself as a cheap initial step before frame extraction or scene detection.
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 states 'Passo inicial barato antes de extrair frames ou detectar cenas,' clearly indicating when to use the tool as a preliminary step. It does not explicitly exclude other contexts, but the context is well implied.
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:

[![mcp-video-vision MCP server](https://glama.ai/mcp/servers/danusiojr/mcp-video-vision/badges/card.svg)](https://glama.ai/mcp/servers/danusiojr/mcp-video-vision)

Score Badge

Copy to your README.md:

[![mcp-video-vision MCP server](https://glama.ai/mcp/servers/danusiojr/mcp-video-vision/badges/score.svg)](https://glama.ai/mcp/servers/danusiojr/mcp-video-vision)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

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/danusiojr/mcp-video-vision'

If you have feedback or need assistance with the MCP directory API, please join our Discord server