Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@C Dance Prompt MCP Search the prompt gallery for cinematic product close-up videos"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
C Dance Prompt MCP is a read-only MCP server for Seedance 2.0 video prompt research. It lets Codex, Claude Code, Cursor, OpenClaw, and other MCP hosts search the public C Dance AI prompt gallery, inspect complete prompts, compare production workflows, and turn rough ideas into structured video prompt briefs.
Prompt gallery: https://cdance.ai/prompts/seedance-2-0
Prompt Architect docs: https://cdance.ai/docs/seedance-video-prompt-architect
OpenClaw skill source: openclaw/seedance-video-prompt-architect/SKILL.md
Chinese README: README.zh-CN.md
The server is intentionally read-only. It does not generate videos, spend credits, create accounts, or require an API key.
Tool | Purpose |
| Search public Seedance 2.0 examples by query, language, or featured status. |
| Fetch one complete sanitized prompt by slug. |
| Compare practical Seedance workflows and discover useful search terms. |
| Turn a rough idea into a deterministic video prompt brief. |
Every prompt result includes its canonical C Dance AI source page.
Related MCP server: Enhanced Multimedia Analysis MCP
{
"mcpServers": {
"cdance": {
"command": "npx",
"args": ["-y", "github:gpt-img-2/cdance-prompt-mcp"]
}
}
}Codex:
codex mcp add cdance -- npx -y github:gpt-img-2/cdance-prompt-mcpOpenClaw:
openclaw mcp add cdance \
--command npx \
--arg -y \
--arg github:gpt-img-2/cdance-prompt-mcp \
--include 'search_prompts,get_prompt,list_workflows,build_video_prompt_brief'
openclaw mcp doctor cdance --probeVariable | Default | Purpose |
|
| C Dance AI prompt API origin. |
|
| Origin used for canonical source links. |
|
| Prompt API timeout from 1,000 to 60,000 ms. |
pnpm install
pnpm validate
pnpm smoke:livepnpm validate runs type checking, unit tests, a build, and a local MCP handshake/tool-call smoke test. pnpm smoke:live additionally verifies the public C Dance AI prompt API.
Turn a rough video idea into a structured, deterministic Seedance prompt brief with motion, camera, continuity, audio, and revision guidance. This does not generate video.
| Name | Required | Description | Default |
|---|---|---|---|
| idea | Yes | ||
| audio | No | ||
| style | No | ||
| camera | No | ||
| useCase | No | ||
| duration | No | ||
| workflow | No | text-to-video | |
| aspectRatio | No | ||
| referenceConstraints | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, non-destructive behavior, so the bar is lower. The description adds meaningful context with 'deterministic' and clarifies that it does not actually generate video, which is useful beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single front-loaded sentence that efficiently states purpose and key output components, followed by a short clarifying disclaimer. Every sentence earns its place with no redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 9 parameters, 0% schema coverage, and no output schema, this description is too high-level. It gives a good overview but does not explain how parameters affect the generated brief, nor does it describe the brief's structure well enough for an agent to confidently choose and invoke the tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
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 9 undocumented parameters. It mentions 'camera' and 'audio' as output areas and 'rough video idea' for the required 'idea' parameter, but it does not explain parameters like useCase, workflow, aspectRatio, or referenceConstraints, leaving significant ambiguity.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Turn') with a clear resource ('a rough video idea') and output ('a structured, deterministic Seedance prompt brief'). It also explicitly states what it does not do ('does not generate video'), distinguishing it from other tools and clarifying scope.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies when to use it: when you have a rough video idea and need a structured prompt brief. However, it does not explicitly compare against siblings like search_prompts or get_prompt, nor does it state conditions for choosing this tool over alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get one complete sanitized Seedance 2.0 prompt and its media by slug. Use a slug returned by search_prompts.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, openWorldHint, and destructiveHint=false, so the safety profile is covered. The description adds useful context by noting the prompt is 'sanitized' and that media is included, but it does not describe edge-case behavior such as missing or invalid slugs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
"The description is two short sentences with no filler. The key identifier-and-action information is front-loaded, and the usage instruction follows immediately. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple single-parameter retrieval tool with rich annotations and no output schema, the description provides the essential workflow context: what is returned, how to identify it, and where the slug comes from. Nothing critical is missing for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0%, so the description carries the burden of explaining the 'slug' parameter. It does so by stating the slug identifies the prompt and should come from search_prompts, which gives the agent enough semantic understanding to invoke the tool correctly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states a specific action ('Get'), a specific resource ('one complete sanitized Seedance 2.0 prompt and its media'), and the lookup mechanism ('by slug'). This distinguishes it from search_prompts, which finds prompts rather than retrieving a single one.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly instructs the agent to use a slug returned by search_prompts, which establishes the correct workflow and source for the identifier. It does not explicitly name alternatives or say when not to use this tool, but the slug-from-search guidance is clear and actionable.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
List practical Seedance 2.0 creation workflows with search queries for finding relevant public examples.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint, destructiveHint=false), so the bar is lower. The description adds useful context about return content — workflows paired with search queries for finding public examples — but says nothing about output format, ordering, or scope limits. This is a modest contribution beyond the annotations, not a rich one.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single 16-word sentence with no filler. The core action ('List practical Seedance 2.0 creation workflows') is front-loaded, and the detail about search queries is appended without bloating the definition.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only listing tool with rich annotations, the description covers the essentials. Since there is no output schema, the disclosure that results include search queries carries meaningful weight. A note on the exact output structure would improve it, but it is a minor gap for such a simple tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100%, so the input contract is fully specified by the schema. Per the baseline for zero-parameter tools, the description need not explain parameters, and its silence on the topic is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description names a specific verb ('List') and resource ('practical Seedance 2.0 creation workflows') and adds that it returns search queries for finding public examples. It is implicitly differentiated from siblings (search_prompts and get_prompt target individual prompts, not workflows), but no explicit distinction is drawn.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No when-to-use guidance is provided. The description never addresses how list_workflows relates to search_prompts, get_prompt, or build_video_prompt_brief, leaving an agent to guess which tool is appropriate for a given intent. There are no exclusions, prerequisites, or alternative-routing statements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search the public C Dance AI Seedance 2.0 prompt gallery. Returns concise previews, video thumbnails, and canonical source pages. Read-only and free.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | No | ||
| language | No | all | |
| featuredOnly | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds useful context by specifying that the gallery is public and free, and by outlining the response contents (previews, thumbnails, source pages). No contradictions with annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two tight sentences with no filler. The search action and resource are front-loaded, return contents are summarized, and the read-only/free note is helpful extra context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple search tool this is adequate: it names the resource, indicates read-only/free behavior, and summarizes outputs. However, it lacks explicit guidance for the four optional parameters, does not mention pagination or result ordering, and does not route users to sibling tools for single-prompt retrieval or workflow listing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0% and the description provides no direct guidance on query, limit, language, or featuredOnly. While parameter names are reasonably self-explanatory, the description does not compensate for the complete lack of schema descriptions, especially for language and featuredOnly.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb ('Search'), a clear resource (the public C Dance AI Seedance 2.0 prompt gallery), and describes the key return items (previews, video thumbnails, canonical source pages). This clearly distinguishes it from siblings like list_workflows and get_prompt by resource and operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides clear context: this is for searching a public, read-only, free prompt gallery. It does not explicitly contrast with alternatives like get_prompt or build_video_prompt_brief, but the intended use case is evident and no misleading exclusions are present.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v1.0.0build_video_prompt_briefget_promptlist_workflowssearch_promptsScored across 4 tools
Each tool has a clear role: listing workflows, searching the prompt gallery, retrieving a specific prompt, and building a new prompt brief. The only mild overlap is between list_workflows and search_prompts, since both are discovery-oriented, but their descriptions distinguish workflows from gallery prompts.
All tool names follow a consistent snake_case verb_noun pattern: list_workflows, search_prompts, get_prompt, build_video_prompt_brief. The naming style and verb usage are uniform, making expected behavior predictable.
Four tools is a well-scoped size for this server's purpose. Each tool covers a necessary step in the prompt discovery and brief-building workflow without redundant or bloated functionality.
The set covers the full intended lifecycle: discovering workflows, searching prompts, retrieving a full prompt by slug, and creating a structured prompt brief. Video generation is explicitly out of scope, so there are no obvious gaps or dead ends for the stated purpose.
Community library of composable AI prompt blocks for search, composition, and contribution.
Manage, version, and publish LLM prompts with blocks, variables, and evaluations.
Create images and videos from prompts, with options for image mixing, reference images, and start/…
Generate contextual prompts and reusable agent skills, evaluate prompts with the 16-dimension Prompt Score, and manage saved work in PromptDrive. Twelve MCP tools also provide authorized access to private Memory for source-grounded answers. Connect over Streamable HTTP using OAuth 2.1 and PKCE. Generation consumes account quota and automatically saves successful results; Memory access follows account permissions and plan limits.
