Provides platform specifications and content adaptation rules for TikTok, enabling repurposing of source content into TikTok-optimized formats including aspect ratio, length, caption style, hashtags, and hook rules.
Click on "Install 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., "@content-repurposing-mcp repurpose this 30-min video for TikTok and YouTube Shorts"
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.
One asset, every platform. The film-once-cut-many engine, with real per-platform adaptation rules.
Disclaimer. Structured repurposing frameworks from documented practice. Platform specs drift; treat numbers as current-practice ranges.
Creators burn out making NEW content daily. The leverage is making ONE strong thing and adapting it correctly for each platform — not reposting the identical file (which the algorithm and the audience both punish). This MCP maps any source asset to every format and gives the exact adaptation rules.
Related MCP server: atlas-social-mcp
Tool | What it returns |
| For a source (long video, podcast, blog, single short), every format it becomes + how to adapt it |
| Aspect, length, caption style, hashtags, hook rule per platform (TikTok, Reels, Shorts, carousel, X thread, LinkedIn, newsletter) |
| The adaptation rules to reshape content for one platform, and what NOT to do |
| The 6 atomic pieces to pull from one asset (pull quotes, clips, stats, steps, story beats, questions) |
| The film-once-cut-many batch workflow, step by step |
| Everything in one payload |
npx content-repurposing-mcp{ "mcpServers": { "content-repurposing": { "command": "npx", "args": ["content-repurposing-mcp"] } } }Hook it (content-hooks-mcp) → repurpose it (this one) → schedule it (social-media-manager-mcp) → own the audience (newsletter-growth-mcp). Full catalog: MCP Hub
Elisabeth Hitz — 10+ years enterprise sales, reverse-engineered for creators.
License: MIT
Given a target platform, returns the adaptation rules to reshape a piece of content for it (aspect, length, hook approach, caption style, hashtags, what NOT to do). Platforms: tiktok, reels, shorts, reel_carousel, x_thread, linkedin, newsletter.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist, so the description carries full burden. It fully discloses what is returned (rules, platform list) and implies read-only behavior. However, it does not mention authentication needs or rate limits, though these are likely minimal for a lookup tool.
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 concise sentences with front-loaded purpose. Every word adds value, listing both the action and the output elements.
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 tool with one parameter, no output schema, and no nested objects, the description completely explains the tool's function and output. No gaps remain.
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% (description does not repeat parameter details), but the description adds meaning by listing the valid platforms and clarifying that the parameter is the target platform. This goes beyond the enum list in the schema.
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 the tool returns adaptation rules for reshaping content given a target platform, listing specific rule types (aspect, length, etc.) and all supported platforms. This distinguishes it from siblings like 'extract_atomic_content' and 'get_platform_specs'.
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 indicates when to use ('Given a target platform') but does not provide exclusions or explicitly mention alternatives among siblings. Context is clear but lacks guidance on when not 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.
The 6 atomic content types you can pull from one long asset (pull quotes, clips, stats/receipts, steps/lists, story beats, questions) and what each becomes. Use to break one asset into many posts.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
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 disclosure. However, it only describes the conceptual purpose and does not mention side effects, permissions, rate limits, or whether the operation is read-only. The description implies a read operation but does not confirm it.
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 sentences with no wasted words. The essential information (list of content types and purpose) is front-loaded. Highly efficient.
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 tool with 0 parameters and no output schema, the description covers the purpose but lacks details on return format or behavior. It is adequate for a simple tool but could be more specific about what the tool outputs.
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 input schema is empty, and schema description coverage is 100% (trivially). With 0 parameters, the baseline score is 4. The description does not need to add parameter meaning, and it does not detract.
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 identifies the verb (extract) and the resource (atomic content types from a long asset), listing the specific types. It distinguishes itself from sibling tools by focusing on breaking one asset into many posts, which is a distinct use case from adaptation or full pack retrieval.
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 states the primary use case: 'Use to break one asset into many posts.' It provides clear context for when to use this tool, but does not explicitly mention when not to use it or offer alternatives. The sibling tool names serve as implicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The complete repurposing library in one payload: repurpose maps, platform specs, atomic types. For full agent context.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses that the tool returns a payload with three components, but it does not mention any side effects, authorization requirements, rate limits, or idempotency. Minimal behavioral disclosure.
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 concise sentence of 17 words, front-loaded with the tool's purpose. 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?
The description names the components of the payload but does not elaborate on structure, format, or whether the data is filtered or aggregated. Without an output schema, more detail would aid completeness.
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?
There are zero parameters, so baseline is 4. The description adds no parameter meaning, but no parameters exist.
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 the tool returns the complete repurposing library in one payload, listing the included components (repurpose maps, platform specs, atomic types). This differentiates it from sibling tools that return individual pieces.
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 phrase 'For full agent context' implies using this tool when the agent needs all repurposing data at once. While it provides context, it lacks explicit when-not-to-use guidance or mentions of alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Platform specs for adapting content: aspect ratio, ideal length, caption style, hashtag norms, hook rule, cadence note. Platforms: tiktok, reels, shorts, reel_carousel, x_thread, linkedin, newsletter. Omit for all.
| Name | Required | Description | Default |
|---|---|---|---|
| platform | No |
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 does not explicitly state that the tool is read-only or has no side effects, though it appears safe. The description could mention that it only retrieves specifications without modifying anything.
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 sentence listing the return fields and platforms, which is concise. However, it could benefit from better structure (e.g., separating return fields and usage instructions).
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?
Given one optional parameter, no output schema, and no annotations, the description adequately explains what the tool returns and how to use the platform parameter. It is fairly complete for the tool's simplicity.
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?
With 0% schema description coverage, the description adds meaning by listing the enum values and explaining that omitting the platform parameter returns specs for all platforms. This goes beyond the enum names in the schema.
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 the tool provides platform specs for content adaptation, listing specific attributes (aspect ratio, ideal length, etc.) and platforms. It distinguishes from sibling tools like adapt_for_platform, which likely performs the adaptation.
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 usage by stating 'Omit for all' for the platform parameter, but does not explicitly state when to use this tool versus alternatives like adapt_for_platform. It lacks a clear when-to-use/when-not-to-use guide.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a source content type (long_video, podcast_episode, blog_or_essay, single_short_video), returns every format it can become and exactly how to adapt it. Omit source for all maps.
| Name | Required | Description | Default |
|---|---|---|---|
| source | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It describes the tool's function but does not disclose side effects, auth needs, rate limits, or output structure. The behavior is implied as read-only lookup.
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 sentences contain all necessary information: purpose with detailed enum values and a specific instruction ('Omit source for all maps'). No wasted words.
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 lookup tool with one parameter and no output schema, the description is fairly complete. It covers input semantics and expected output concept, though output structure details are missing.
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 description lists the allowed enum values for the single parameter 'source', adding context beyond the schema's enum definition. Schema coverage is 0%, so the description compensates well.
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 the tool returns every format a source content type can become and how to adapt it, listing specific source types. This distinguishes it from siblings like adapt_for_platform or get_repurpose_workflow.
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 usage when mapping a source type to all formats, but does not explicitly state when not to use it or compare to alternatives. The phrase 'Omit source for all maps' provides some usage nuance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The 'film once, cut many' batch workflow: capture one strong asset, extract atomic pieces, adapt each per platform, schedule across the week. Returns the step-by-step.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description only mentions returning a step-by-step workflow. It does not disclose behavioral traits like being read-only, required permissions, or any side effects, which the agent needs to know.
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 sentence describes the workflow concisely, but it is slightly verbose. It is well front-loaded with the key concept.
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?
Given no output schema and minimal description, the agent lacks details about what the step-by-step output contains and how it integrates with sibling tools. More context is needed for effective decision-making.
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?
There are no parameters, so schema coverage is 100%. The description does not need to add parameter semantics, and the baseline score of 4 applies.
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 explicitly states the tool returns the step-by-step repurposing workflow, distinguishing it from sibling tools that focus on specific steps like adapting or extracting. However, the exact nature of the output is not fully specified.
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 guidance on when to use this tool versus siblings such as adapt_for_platform or get_repurpose_map. The agent is left to infer context without explicit when-to-use or when-not-to-use instructions.
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. Dates show when Glama detected each change.
v1.0.0adapt_for_platformextract_atomic_contentget_full_packget_platform_specsget_repurpose_mapget_repurpose_workflowEach tool has a distinct purpose: adapting for a platform, extracting atomic content, getting full context, platform specs, repurpose maps, and workflows. However, `get_full_pack` aggregates outputs of other getters, causing minor potential confusion.
All tools follow a `verb_noun` pattern with snake_case. Most use `get_`, but `adapt_for_platform` and `extract_atomic_content` use different verbs, which is acceptable. Naming is clear and predictable.
With 6 tools, the server is well-scoped for content repurposing guidance. Each tool covers an essential aspect without redundancy or bloat.
The tool set covers the full lifecycle: breaking down content (extract_atomic_content), platform adaptation (adapt_for_platform, get_platform_specs), repurpose mapping (get_repurpose_map), and workflow (get_repurpose_workflow). No obvious gaps.
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If you are the server author, to access and configure the admin panel.
Turn long videos into AI-curated short clips: caption, reframe, thumbnail, schedule, and publish.
Turn any video or livestream into scored, captioned, ready-to-post vertical clips.
Turn long videos into viral vertical shorts and publish them to TikTok, Instagram and YouTube.
Generate AI short-form videos and auto-publish them to TikTok, YouTube, Instagram, Facebook and X.
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