Sora 2 MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no ambiguity. The tools cover different aspects of video generation and management: creation (create_video, create_fade_animation), status checking (get_video_status), listing (list_videos), deletion (delete_video), and merging (merge_videos). There is no overlap in functionality that would cause misselection.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case. The verbs (create, delete, get, list, merge) are clear and appropriate for their actions, and the nouns (fade_animation, video, video_status, videos) accurately describe the resources. There are no deviations or mixed conventions.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose of video generation and management. Each tool earns its place by covering essential operations: creating videos, checking status, listing jobs, deleting jobs, and merging videos. This count is neither too thin nor too heavy for the domain.
Completeness5/5
The tool set provides complete CRUD/lifecycle coverage for video generation jobs. It includes creation (create_video, create_fade_animation), reading (get_video_status, list_videos), deletion (delete_video), and additional utility (merge_videos). There are no obvious gaps, and agents can handle the full workflow from generation to management.
Average 3.1/5 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
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions the creation action but lacks critical details: it doesn't specify whether this is a synchronous or asynchronous operation (unlike 'get_video_status' which suggests async), doesn't mention rate limits, costs, permissions needed, or what happens on failure. The description is insufficient for a mutation tool with zero annotation coverage.
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 appropriately concise with two sentences that efficiently convey the core functionality. The first sentence states the main purpose, and the second adds parameter context without unnecessary elaboration. It could be slightly more structured by front-loading more critical behavioral 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?
For a video generation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what the tool returns (e.g., video URL, job ID, error formats), doesn't address async behavior hinted at by sibling 'get_video_status', and provides minimal guidance on parameter trade-offs despite the schema doing most of the work.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description adds minimal value by mentioning 'optional parameters for resolution, duration, and model selection' but doesn't provide additional semantic context beyond what's in the schema. This meets the baseline for high schema coverage.
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 action ('Generate a video') and the resource ('using Sora 2 or Sora 2 Pro model'), with specific details about creating from a text prompt. It distinguishes from siblings like 'create_fade_animation' by specifying the model type, but doesn't explicitly contrast with other video-related tools like 'merge_videos' or 'delete_video'.
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 'create_fade_animation' or 'merge_videos'. It mentions optional parameters but doesn't explain scenarios where one might choose this over sibling tools, leaving the agent without contextual usage direction.
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 behavioral disclosure. It states the tool deletes a job and its data, implying a destructive mutation, but lacks details on permissions required, whether deletion is reversible, rate limits, or error handling. This is a significant gap for a destructive operation.
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, efficient sentence that front-loads the key action ('Delete') and resource. There is no wasted verbiage, making it easy for an agent to parse quickly and accurately.
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 destructive nature, lack of annotations, and no output schema, the description is incomplete. It doesn't cover behavioral aspects like side effects, success/error responses, or usage constraints, which are crucial for safe and effective tool invocation in this 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?
The input schema has 100% description coverage, with the 'video_id' parameter clearly documented. The description doesn't add any meaning beyond the schema (e.g., format examples or source of the ID), so it meets the baseline score of 3 where the schema does the heavy lifting.
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 action ('Delete') and resource ('a video generation job and its associated data'), making the purpose immediately understandable. However, it doesn't explicitly differentiate this from sibling tools like 'list_videos' or 'get_video_status', which are read operations, so it misses the highest score.
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. It doesn't mention prerequisites (e.g., needing an existing video ID), exclusions (e.g., cannot delete in-progress jobs), or comparisons to siblings like 'create_video' or 'merge_videos', leaving the agent with minimal 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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions that the tool creates a video and describes the fade effect, but lacks critical behavioral details: it doesn't specify file format requirements, performance characteristics, error handling, or what happens if the output path already exists. For a creation tool with zero annotation coverage, this leaves significant gaps in understanding its 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 extremely concise with just two sentences that directly state the tool's function and effect. Every word earns its place—there's no redundancy or unnecessary elaboration. It's front-loaded with the core purpose and follows with a clear explanation of the animation behavior.
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 moderate complexity (4 parameters, creation operation) and lack of annotations/output schema, the description is minimally adequate. It explains what the tool does but doesn't cover behavioral aspects, error conditions, or output details. For a video creation tool, users would benefit from more context about supported formats, performance, and result verification.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all four parameters thoroughly. The description mentions 'fade-in' specifically, which aligns with the default 'fade_type' value but doesn't add meaningful semantic context beyond what's in the schema. It doesn't explain parameter interactions or provide usage examples, so it meets the baseline for high schema coverage.
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's purpose: 'Create a fade-in animation video from a static image.' It specifies the verb ('create'), resource ('fade-in animation video'), and source material ('static image'). However, it doesn't explicitly distinguish this from sibling tools like 'create_video' or 'merge_videos' beyond mentioning the fade effect.
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. It doesn't mention sibling tools like 'create_video' (which might handle other animation types) or 'merge_videos' (which could combine videos). There's no context about prerequisites, limitations, or appropriate scenarios for fade animations versus other video creation methods.
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 full burden for behavioral disclosure. It mentions filtering and pagination but doesn't specify what filtering options exist, whether results are sorted, if there are rate limits, authentication requirements, or what the return format looks like. For a list operation with no annotation coverage, this leaves significant 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that states the core functionality upfront. It's appropriately sized for a simple list tool, though it could potentially be more structured by separating filtering and pagination aspects into distinct clauses for clarity.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what information is returned about each video job, how filtering works, what fields are available, or what the response structure looks like. For a tool that presumably returns multiple video records, this leaves the agent with insufficient context about what to expect.
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?
The schema description coverage is 100%, with both parameters well-documented in the input schema. The description adds minimal value beyond the schema by mentioning 'optional filtering and pagination' but doesn't provide additional semantic context about parameter usage, relationships, or constraints beyond what's already in the structured 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 clearly states the tool's purpose as listing video generation jobs with filtering and pagination capabilities. It specifies the resource (video generation jobs) and verb (list), but doesn't differentiate from sibling tools like 'get_video_status' which might also retrieve video information.
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 context through 'optional filtering and pagination' but doesn't explicitly state when to use this tool versus alternatives like 'get_video_status' for individual videos or 'create_video' for new jobs. No explicit when-not-to-use guidance or alternative recommendations 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 mentions the tool returns status information (processing, completed, failed), which covers basic output behavior. However, it lacks details on error handling, rate limits, authentication needs, or what specific details are included beyond status. For a tool with no annotations, this is a significant gap in transparency.
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 concise and front-loaded, stating the purpose in the first sentence and elaborating on return values in the second. Both sentences earn their place by clarifying the tool's function and output. It could be slightly more structured by explicitly separating purpose from behavior, but it remains efficient with no wasted words.
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 low complexity (single parameter, no output schema, no annotations), the description is minimally adequate. It covers the basic purpose and output states but lacks depth in behavioral context (e.g., error cases or detailed return format). Without an output schema, more explanation of return values would be beneficial, but it meets the minimum viable threshold for this 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?
The input schema has 100% description coverage, with the 'video_id' parameter clearly documented in the schema. The description does not add any additional meaning or context beyond what the schema provides (e.g., format examples or usage tips). According to the rules, with high schema coverage (>80%), the baseline score is 3, as the schema does the heavy lifting.
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's purpose: 'Get the status and details of a video generation job.' It specifies the verb ('Get') and resource ('video generation job'), but does not explicitly differentiate it from sibling tools like 'list_videos' or 'delete_video' beyond the focus on status retrieval. This makes it clear but not fully sibling-distinctive.
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 by stating it returns information about processing, completion, or failure, suggesting it should be used to check job progress. However, it does not provide explicit guidance on when to use this tool versus alternatives (e.g., 'list_videos' for general listing or 'create_video' for initiation), nor does it mention any exclusions or prerequisites for 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 provided, so the description carries the full burden. It mentions the concatenation order, but lacks details on permissions needed, whether the operation is reversible, potential rate limits, or what happens if videos have different formats. This is a significant gap for a mutation tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with zero waste, front-loaded with the core purpose and followed by a key behavioral detail (order of concatenation), making it efficient and easy to parse.
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 mutation tool with no annotations and no output schema, the description is incomplete. It lacks information on error handling, output format, or side effects, which are crucial for an agent to use this tool correctly in 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?
Schema description coverage is 100%, so the schema already documents both parameters thoroughly. The description adds minimal value by implying order matters for 'video_urls', but does not provide additional syntax or format details beyond what the schema specifies.
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 specific action ('merge multiple video files into a single video') and resource ('video files'), distinguishing it from siblings like 'create_video' or 'delete_video' by focusing on concatenation rather than creation or deletion.
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 by stating videos are concatenated in order, but it does not provide explicit guidance on when to use this tool versus alternatives like 'create_fade_animation' or 'create_video', nor does it mention prerequisites or exclusions.
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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