mcp-ffmpeg

by priyanshum143

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Latest release: v0.1.6
Disambiguation5/5
Each tool has a clearly distinct purpose: status retrieval, result retrieval, and separate processing jobs for audio extraction, format change, resolution change, subtitle format change, trimming, and transcription. No overlap in functionality.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern: get_ for retrieval and start_ for job enqueuing. Underscores are used uniformly, making the naming predictable.
Tool Count5/5
With 8 tools, the server covers a reasonable set of common FFmpeg operations without being overwhelming. The count is well-scoped for a media processing server.
Completeness4/5
The tool set covers core operations like status check, result retrieval, audio extraction, format conversion, resolution change, subtitle conversion, trimming, and transcription. Missing tools for video compression, frame extraction, or concatenation, but these are minor gaps given the common use cases.
Average 4/5 across 8 of 8 tools scored. Lowest: 3/5.
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
- Last stable release on April 13, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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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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 are provided, so the description carries full burden. It only implies a read operation ('Get') but does not disclose any behavioral traits such as required authentication, rate limits, or whether it is destructive.
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 concise sentence that immediately states the purpose. No unnecessary words or 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?
Given the tool's simplicity (one parameter, output schema exists), the description provides the basic purpose. However, it lacks context about what 'status' means or possible usage scenarios, making it minimally complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description adds no meaning beyond the schema's 'title: Job Id'. It fails to compensate for the lack of parameter documentation.
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 verb 'Get' and the resource 'latest status for a job_id', distinguishing it from sibling tool 'get_job_result' which returns results, and from 'start_*' tools which initiate actions.
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 'get_job_result'. Does not specify prerequisites (e.g., job must exist) or conditions for use.
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. Description mentions non-re-encoding, caching behavior via force_run, and immediate return of job_id+status. However, it doesn't disclose potential side effects (e.g., overwriting files) or permissions.
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?
Description is concise: three sentences plus parameter explanations, front-loaded with core action. No redundant information.
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?
Covers core functionality and parameters. Mentions async nature via 'Returns immediately with job_id + status'. Output schema exists, so return values not needed. Lacks details on output file location or persistence.
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?
Schema coverage is 0%, but the description fully defines each parameter: input_file must exist, output_format with examples and optional dot, and force_run behavior. This adds essential meaning beyond the schema titles.
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 converts video to another container format without re-encoding, using specific verbs 'Enqueue' and 'Converts'. It distinguishes from siblings like start_change_resolution by focusing on container format change.
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 vs alternatives like start_change_resolution or start_audio_extraction. Does not specify prerequisites or context.
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?
Since annotations are missing, the description carries full burden. It discloses the async behavior (returns immediately with job_id+status) and the force_run option for caching. However, it doesn't mention error handling or required permissions.
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 action and return behavior. It lists parameters with brief explanations without unnecessary fluff. Every sentence adds value.
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?
Given the output schema exists (though not shown), the description adequately explains the async return with job_id+status. Sibling tools for checking job results and status provide complete context for the workflow.
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 0%, so the description adds meaningful context for parameters: 'path to input video file (must exist)' and 'target subtitle format'. The force_run parameter is also explained. This compensates for the lack of schema 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?
The description clearly states it enqueues a job to convert subtitle format, using a specific verb and resource. This distinguishes it from sibling tools like start_audio_extraction or start_change_format.
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 given on when to use this tool versus alternatives, such as start_change_format or other conversion tools. No conditions 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.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Describes asynchronous enqueue behavior and force_run's cache-ignoring effect. No annotations exist. Missing details on failure modes, resource consumption, or whether trimming modifies original file.
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: first states purpose and return, second lists parameters with inline clarifications. No redundant words, front-loaded with core action.
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?
Covers all parameters, mentions return format, and assumes output schema handles details. Lacks mention of possible side effects or file size considerations, but sufficient for a simple trim 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?
Schema has 0% description coverage; description adds critical context: input_file must exist, times are floats in seconds, duration defined, force_run for cache bypass. Fully compensates schema 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?
Description clearly states 'Enqueue a TRIM job' with immediate return of job_id+status. It specifies the verb (enqueue/trim) and resource (video file), and differentiates from siblings like start_audio_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?
No explicit when-to-use or when-not-to-use guidance. The parameter listing implies trimming video files, but alternatives like start_change_format are not contrasted. Lacks usage context.
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?
Discloses immediate return with job_id and status, and cache behavior via force_run. Lacks details on permissions, file overwrites, or error scenarios.
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 paragraphs: action/return, then parameter definitions. No unnecessary text.
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?
Covers purpose, parameters, and return value. Lacks output schema details but mentions job_id and status. Sufficient for a simple enqueue job.
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?
With 0% schema coverage, description adds value by explaining input_file requirement, target_format default, and force_run purpose. Compensates well for lack of schema 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 it extracts audio from video in user-requested format. Differentiates from sibling tools like start_trim or start_video_transcription.
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?
Provides basic usage context (input_file must exist, force_run bypasses cache) but does not explicitly compare to alternatives or specify when to use this tool.
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 reveals that the tool is asynchronous ('Returns immediately with job_id + status') and mentions caching behavior via force_run parameter. However, since no annotations are provided, more details about side effects (e.g., file creation, original file modification) would improve 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?
The description is concise with a front-loaded opening sentence followed by bullet-pointed parameter explanations. No redundant information; 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?
Given the presence of an output schema, the description appropriately summarizes return behavior. It covers all parameters and asynchronous nature. Missing explicit differentiation from siblings and potential prerequisites (e.g., supported video formats) but overall complete for a job-enqueueing 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?
All four parameters are described with meaningful explanations beyond the schema: input_file path with existence requirement, height/width as desired dimensions, and force_run caching override. Schema coverage was 0%, so the description fully compensates.
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 enqueues a CHANGE_RESOLUTION job that converts video resolution according to provided height and width. It uses specific verb 'Enqueue' and resource 'CHANGE_RESOLUTION job', distinguishing it from siblings like start_change_format.
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 explains what the tool does but does not explicitly state when to use this tool versus alternatives (e.g., start_change_format for format conversion). Usage is implied by the purpose but lacks explicit guidance.
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?
The description discloses all behavioral traits for different job states (success, queued/running, failed) without relying on any annotations. It fully informs the agent of what to expect, compensating for the lack of 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 concise with four sentences, front-loaded with the primary purpose. Each sentence adds essential information without redundancy, making it efficient for an AI agent to parse.
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?
Given the tool's simplicity (one parameter, output schema present), the description covers all necessary contexts: when to use, what returns for each job status. The output schema handles return value details, so no further elaboration needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters1/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has one parameter (job_id) with 0% schema description coverage. The description does not mention or explain the job_id parameter at all, failing to add meaning beyond the schema's type and title. The tool description should have explained what job_id refers to and how to obtain it.
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 the final output of a completed job using the verb 'Retrieve' and specific resource 'final output of a completed job'. It distinguishes itself from sibling tool get_job_status by focusing on result retrieval rather than status checking.
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?
Explicitly states when to use the tool: 'only be called after the job status is success'. Also describes what happens for other statuses (queued/running returns status, failed returns error details), providing clear guidance on expected behavior in each scenario.
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?
With no annotations, the description fully handles behavioral disclosure. It explains that the tool is asynchronous (returns immediately with job_id and status) and describes the force_run parameter's effect on caching. This is comprehensive.
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 (two short sentences plus bullet-like parameter list) and front-loaded with the core function. 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 the tool has an output schema and no annotations, the description covers the essential aspects: action, async behavior, return, and all parameters. It lacks details on error handling or file size limits, but overall it is sufficient for correct use.
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?
The input schema has 0% description coverage, but the tool description lists all five parameters with their meaning, valid values (e.g., model names, language codes, output formats), and constraints ('must exist' for input_file). This fully compensates for the schema 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 the tool enqueues a transcription job using Whisper on video/audio files, distinguishing it from sibling tools like start_audio_extraction or start_change_format. The verb 'Enqueue' and resource 'transcript job' are specific.
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 the tool is for transcription tasks, but does not explicitly state when to use it versus alternatives like start_audio_extraction or get_job_result. 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.

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