video-mcp

by petercr

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation3/5
Most tools have distinct purposes, but video.create_preview and video.render are explicit aliases of the same operation, creating unnecessary ambiguity. The descriptions clarify the alias, which mitigates confusion, but the duplication is still a flaw.
Naming Consistency4/5
Tool names follow a consistent pattern of domain prefix (video/subtitle/project) followed by an action verb (inspect, transcribe, clean, create_preview). The naming is uniform, though the presence of 'render' as an alias for 'create_preview' introduces slight redundancy without breaking the convention.
Tool Count5/5
With 9 tools, the server is well-scoped for a video captioning workflow. Each tool (except the alias pair) serves a distinct step in the pipeline, and the count is within the ideal 3–15 range.
Completeness4/5
The tool set covers the core video-to-subtitle-to-export workflow: inspect, transcribe, caption, preview, clean, export SRT/ASS, and create an editable project. Minor gaps exist such as lack of an explicit transcript retrieval or deletion/update operations, but agents can work around these with the provided pipeline.
Average 3/5 across 9 of 9 tools scored. Lowest: 2.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 27 commits in the last 12 weeks
- Last stable release on August 12, 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.
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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 exist, so the description must carry the full burden. It mentions 'local' execution and 'complete' pipeline, hinting at on-device processing and multi-step behavior, but it omits important behavioral traits like file creation, overwriting, or required resources. The overwrite and create_preview_output parameters imply side effects that are not disclosed.
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 a single, clean sentence that is easy to read and front-loads the verb. However, it is under-specified given the tool's complexity; the brevity sacrifices essential information, so it is not effective conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 9 parameters, no annotations, and an output schema, the one-sentence description is grossly incomplete. It does not explain the pipeline stages, expected outputs, or relationship to sibling tools, leaving the agent without enough context to invoke the tool correctly.
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?
The input schema has 9 parameters with 0% description coverage, and the description mentions only 'a video,' providing no explanation for style, device, threads, language, overwrite, config_path, preview_width, or create_preview_output. This is far below the coverage threshold and the description does not compensate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description identifies the tool as running a 'complete local caption pipeline' for a video, which conveys a high-level purpose and implies a composite workflow. However, 'caption pipeline' is vague and doesn't explicitly state what steps are involved or what outputs are produced, making it hard to distinguish from sibling tools like video.transcribe or subtitle.clean on first read.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided about when to use this tool versus alternatives such as video.transcribe or video.render. The description does not mention any prerequisites, exclusions, or context in which the complete pipeline should be chosen over individual steps.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of disclosing behavior. It only mentions the core transcription action and the local ASR backend, but does not describe output format, handling of existing files (overwrite), language detection, resource usage, or side effects. The 'normalized audio' hint is useful but insufficient.
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 a single, front-loaded sentence with no unnecessary words. It efficiently communicates the tool's essence. However, given the tool's complexity, a bit more detail could be warranted, but this dimension specifically rewards concise writing, so a 4 is appropriate.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness1/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This tool has 7 parameters, no annotations, and zero schema description coverage. The description provides almost no contextual detail about when to use the tool, how to configure it, or what to expect. The presence of an output schema does not compensate for the lack of guidance on parameters and behavior.
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%, so the description must compensate for parameter meaning. It fails to explain any of the 7 parameters, including the required audio_path. No mention of device, threads, language, overwrite, config_path, or output_path, leaving the agent to guess their roles.
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 'Transcribe' and the resource 'normalized audio', also specifying the local ASR backend. This distinguishes it from sibling tools like video.caption or subtitle.export_srt, which are about captions and subtitle exports, not raw transcription.
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 about when to use this tool versus alternatives. There is no mention of prerequisites (e.g., what 'normalized audio' means or how to obtain it) or exclusions. The existence of sibling tools like video.caption suggests overlapping use cases, but the description does not address them.
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?
There are no annotations, so the description must disclose behavioral traits. It only states the core function and does not mention side effects, default behaviors, or requirements. For example, it does not clarify what happens if the output file already exists or how config_path affects behavior.
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, focused sentence that immediately conveys the tool's purpose. It is appropriately sized and front-loaded, with no unnecessary 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?
Despite having an output schema, the tool has 4 parameters and no annotations. The description is too sparse to provide complete context, lacking details about default output locations, overwrite behavior, or how config_path modifies the export. This is a minimal viable description at best.
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 0%, so the description must compensate for explaining parameters. It only hints at transcript_path via 'transcript JSON file' but provides no meaning for overwrite, config_path, or output_path, leaving the agent with minimal guidance on required vs optional 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 clearly states the tool exports a normalized transcript JSON file as SRT, specifying the verb, input resource, and output format. It implicitly differentiates from the sibling tool subtitle.export_ass by naming the output format, but it does not explicitly name that alternative.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives like subtitle.export_ass. It does not mention prerequisites, such as the need for a normalized transcript, nor does it explain the role of parameters like overwrite or config_path.
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?
The description discloses the execution modes ('optional local LLM and deterministic fallback'), which gives some insight into behavior. However, it does not mention side effects like overwriting the input file (despite the 'overwrite' parameter), output handling, or any requirements. Without annotations, this is only partial 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 a single concise sentence with no redundant or filler content. It is appropriately brief and front-loaded, conveying the essential action quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 4 parameters, no parameter descriptions, and a potentially nuanced behavior (optional LLM vs fallback), the description is too minimal to be complete. It does not explain the cleaning process, parameter interactions, or the importance of the fallback, making it insufficient for reliable agent selection and invocation.
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?
The schema has 0% description coverage, and the description does not mention any parameters. It does not clarify the meaning or role of transcript_path, overwrite, config_path, or output_path, so it adds no value beyond the raw schema.
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 uses a specific verb ('clean') and resource ('transcript'), and the action is distinct from sibling tools like subtitle.export_srt or video.transcribe. However, it does not define what 'clean' entails (e.g., removing filler words, formatting), so it is clear on surface but not deeply distinctive.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool instead of alternatives, nor any context such as prerequisites or typical use cases. It is a single declarative sentence without any conditional or comparative information.
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 carry the full burden of behavioral transparency. It only states the core export action and does not mention overwrite behavior, config file handling, default styles, or output path defaults. Users are left unaware of side effects or operational constraints.
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 front-loads the key information. Every word contributes to the meaning, with no filler or redundancy. It is appropriately brief for a tool with a clear primary function.
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 tool's complexity (7 parameters, no annotations, and no schema descriptions), the one-line description is insufficient. It does not address configuration, output handling, or operational nuances. The presence of an output schema does not compensate for the lack of guidance on inputs and behavior.
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 does not compensate by explaining any of the 7 parameters. It mentions 'normalized transcript JSON' and 'styled ASS' but gives no insight into parameters like style, width, height, overwrite, config_path, or output_path. This leaves the agent with no additional semantic understanding beyond the bare 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 function: exporting a normalized transcript JSON file as styled ASS. The verb 'export' specifies the action, the resource is the transcript JSON, and the output format is ASS. This distinguishes it from sibling tools like subtitle.export_srt, which exports SRT, and subtitle.clean, which cleans subtitles.
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 converting a normalized transcript to ASS subtitles, but it does not explicitly state when to use it versus alternatives like subtitle.export_srt. The word 'normalized' hints at a prerequisite (e.g., prior cleaning), but this is not made explicit. No exclusions or alternative references are provided.
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 the full burden of behavioral disclosure. It only says 'create', but does not mention overwrite behavior, output path handling, side effects on input files, or permission requirements. Key parameters like overwrite and output_path are left unexplained.
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, front-loaded sentence with no wasted words. It immediately states the action and the required inputs, earning a perfect score for conciseness and structure.
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?
While an output schema exists, the description gives no workflow context, prerequisites, or behavior around the optional parameters. For a tool with five parameters and no annotations, this minimal description is insufficient for an agent to invoke it correctly, especially without understanding overwrite and output_path semantics.
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 0%, and the description names none of the five parameters. Even though parameter names like input_path and subtitles_path are somewhat self-explanatory, the description adds no semantic detail about how these parameters interact or what values are expected, failing to compensate for the schema's lack of 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 uses a specific verb ('Create') and resource ('editable Kdenlive project') with clear input sources ('video and SRT file'), which makes the tool's purpose unmistakable. It also distinguishes from sibling tools like video.transcribe or video.caption, which perform different operations.
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 creating an editable project from an existing video and SRT, but it does not explicitly state when to use this tool versus alternatives. No exclusions or alternative tool references are provided, making it minimally adequate.
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, the description carries full burden. It states 'inspect' and 'FFprobe', implying a read-only operation, but does not explicitly disclose lack of side effects, configuration behavior, dependencies, or potential errors. More behavioral context is needed.
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, front-loaded sentence with no wasted words. It efficiently conveys the tool's core action and output.
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 tool appears simple and output schema exists, so return values are covered. However, the description omits the purpose of config_path, any usage caveats, and what 'normalized' implies, leaving the context incomplete for an agent to fully understand configuration and edge cases.
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 0%, and the description does not explain either parameter. 'input_path' is only vaguely implied by 'video', while 'config_path' is entirely unaddressed. The description must compensate for the schema's lack of parameter details but 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 uses a specific verb 'inspect', identifies the resource 'a video', specifies the method 'with FFprobe', and states the output 'normalized media metadata'. This clearly distinguishes it from sibling tools like transcribe, caption, or render.
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 use for inspecting video metadata but provides no explicit guidance on when to use this tool versus alternatives. There is no mention of exclusions or preferred scenarios, leaving usage largely inferred from the purpose.
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 must disclose behavior itself. It states the output type (MP4), the operation (burning subtitles), and the quality/speed tradeoff (fast, downscaled). However, it does not explain side effects such as file creation behavior, default output paths, overwrite semantics, or how configuration files affect the process, leaving meaningful behavioral gaps.
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, well-structured sentence that front-loads the action ('Burn') and contains no filler. Every word contributes to understanding the tool's core purpose.
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 six parameters, two required, and no annotations, the description is too sparse. It does not explain the purpose of config_path, how output paths are determined, overwrite behavior, or preview sizing specifics, leaving the agent with significant missing context despite the presence of an output schema.
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 input schema has zero description coverage, so the description must compensate. It only implicitly hints at subtitles_path (ASS subtitles) and preview_width (downscaled), but leaves input_path, output_path, config_path, and overwrite unaddressed, providing minimal parameter-level guidance.
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 uses a specific verb 'Burn' and identifies the exact resource and transformation: ASS subtitles are burned into a downscaled MP4 preview. This clearly distinguishes it from sibling tools like video.render (final output) and video.caption (caption generation) by specifying the preview nature and subtitles input.
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 does not explicitly state when to use this tool versus alternatives like video.render. However, the word 'preview' and adjectives 'fast' and 'downscaled' imply it is intended for quick, low-quality preview rendering, which is an indirectly conveyed usage context.
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 must disclose behavioral traits. 'Render' implies file generation, but the description does not mention side effects such as creating/overwriting files, required permissions, or whether the operation is reversible. The alias hint provides some context but lacks essential safety and mutation details.
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, front-loaded sentence that immediately states the action and resource, followed by an alias clarification. No wasted words; structured effectively for fast parsing.
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 having an output schema, the description lacks context for a 6-parameter tool with no annotations. It does not mention prerequisites, configuration behavior, or output path defaults, and the alias to video.create_preview only partially mitigates this by pointing to a sibling tool. The description is too sparse to be fully actionable in a complex workflow.
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 0%, so the description must compensate. It vaguely implies input_path and subtitles_path via 'input video' and 'ASS subtitles', but it does not explain overwrite, config_path, output_path, or preview_width. The parameter names alone carry most of the meaning, but the description adds minimal semantic value.
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 uses a specific verb ('Render') with a clear resource ('burned-in ASS subtitle preview') and explicitly identifies itself as an alias for video.create_preview, which distinguishes it from sibling tools like video.caption or video.transcribe. This makes the tool's purpose unambiguous.
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 clearly indicates this is equivalent to video.create_preview, providing a direct alternative. However, it does not explicitly state when to use this over other preview-related tools or mention exclusions (e.g., 'use video.caption for caption-only output').
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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