ffmpeg-mcp
Server Quality Checklist
Latest release: v1.0.2
- Disambiguation5/5
Each tool targets a distinct FFmpeg operation: concatenation, conversion, cutting, raw commands, image-to-video, and silence removal. No overlap; ffmpeg_raw is explicitly a catch-all for advanced cases, maintaining clear boundaries.
Naming Consistency4/5Tool names mostly follow a verb_noun pattern in snake_case (e.g., concat_videos, cut_video, remove_silence). Convert is slightly generic but consistent with the pattern. Overall naming is predictable and readable.
Tool Count5/56 tools is well-scoped for an FFmpeg server. It covers common operations without being overwhelming, and each tool has a clear purpose. The count is appropriate for the domain.
Completeness4/5The set covers core operations: concatenation, conversion, cutting, image-to-video, and silence removal. Missing some specific tasks like subtitles or scaling, but ffmpeg_raw fills gaps for advanced needs. Minor gaps remain.
Average 3.2/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 0 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI status not available
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?
No annotations provided, and the description discloses no behavioral traits such as whether it overwrites the output file, supports specific codecs, or the order of concatenation. This leaves the agent uninformed about side effects or requirements.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that directly conveys the tool's purpose. No unnecessary information, though a bit more detail could be added without sacrificing conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with two parameters and no output schema, the description is minimally adequate. However, it lacks details on error handling, output format, or any behavioral important for the agent to use it correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema covers both parameters with descriptions. The tool description adds no further meaning beyond the schema, but the schema coverage is complete, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool concatenates multiple videos into one, using a specific verb and resource. It distinguishes from siblings like cut_video or convert, but could be improved by mentioning ordering or format compatibility.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 ffmpeg_raw or convert. The description does not mention prerequisites or scenarios where concatenation is appropriate.
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, description carries full burden but only states basic functionality. Missing details on how silent segments are removed (e.g., splicing, speed changes), output consequences, 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.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, no redundancy. However, it could include additional useful context without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and no annotations. Description lacks explanations of return value, behavior for edge cases, or how parameters interact. Incomplete for a tool with 4 parameters.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so baseline is 3. The description adds no additional meaning beyond the parameter descriptions already present in the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool detects and removes silent segments from a video, keeping only non-silent parts. It uses specific verbs and resource, and distinguishes from siblings like cut_video which cuts by time or frames.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 cut_video or ffmpeg_raw. Does not mention prerequisites, when not to use, or typical use cases.
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 provided. Description only mentions auto-detection of format from extension, which adds some value, but fails to disclose crucial behavioral traits like whether it overwrites existing files, supported formats, or any 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with the main action and supported conversions. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite schema covering parameters, the description lacks context on expected behavior (e.g., return value, error handling, supported formats) and no output schema. For a conversion tool, it is incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema descriptions cover 100% of parameters, so baseline is 3. The description adds that format is auto-detected, reinforcing outputPath semantics but not providing new meaning beyond schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states it converts media between formats with specific combinations (audio-to-audio, video-to-video, video-to-audio). It is distinct from sibling tools like concat_videos or cut_video, which serve different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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 ffmpeg_raw or image_to_video. Lacks explicit context for usage or when not to use.
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 present, so the description carries the full burden. It only states the action without disclosing behavioral traits such as file handling (non-destructive due to input/output paths), codec support, or error responses. The minimal description fails to inform the agent of important behaviors.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no extraneous words. It efficiently communicates the core purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description lacks context about return values, success criteria, or limitations (e.g., supported video formats). With 4 required parameters and no output schema, the agent needs more information to invoke the tool correctly, such as expected output format or error handling.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, providing descriptions for all 4 parameters. The tool description adds no additional meaning beyond the schema, such as format examples or constraints. Baseline 3 is appropriate given the schema coverage.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Extract a segment from a video file' uses a specific verb and resource, clearly identifying the tool's function. It distinguishes itself from siblings like concat_videos, convert, and remove_silence by focusing on segment extraction.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No usage guidelines are provided. The description does not indicate when to use this tool versus alternatives, nor does it specify prerequisites or limitations. The agent must infer context from the sibling list without explicit guidance.
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 present, and the description does not disclose behavioral traits beyond the basic operation. It does not mention if files are overwritten, required permissions, or how errors are handled. The description carries the full burden but provides 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.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no unnecessary words. It is front-loaded with the essential purpose and efficiently communicates the core functionality.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 4 parameters and no output schema or annotations, the description provides a minimal overview but omits details like supported input formats, aspect ratio handling, and output codec. It is adequate for a straightforward conversion but not fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does 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. The description adds limited value by reiterating 'fixed duration', but does not elaborate on parameter constraints like valid image formats or the effect of fps on output quality.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Convert'), the resource ('static image to a video'), and a key constraint ('fixed duration'). It distinguishes from sibling tools like concat_videos and ffmpeg_raw.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does 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. There is no mention of prerequisites, limitations, or context in which image_to_video is preferred over concat_videos or ffmpeg_raw.
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, and the description fails to disclose critical behavioral traits: no mention of potential side effects (e.g., file creation, overwrites), error handling, or safety warnings for raw command execution.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences efficiently state purpose, usage context, and examples. No redundant information; front-loaded with core function.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Without an output schema, the description should explain what the tool returns or produces (e.g., output files, console output). It does not, leaving significant gaps for an advanced raw execution tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and already describes the args parameter well. The description adds a helpful usage example but no additional semantic detail beyond what the schema provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool executes raw FFmpeg commands and distinguishes itself from sibling tools by specifying it's for advanced operations not covered by others, with concrete examples (filters, effects, etc.).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description advises using this tool for advanced operations not covered by other tools and lists examples. However, it lacks explicit when-not-to-use guidance or direction to specific siblings for simpler tasks.
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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