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., "@Growth Hacker MCP generate hooks for my new AI productivity app launch"
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.
The ultimate Model Context Protocol (MCP) server for Creators, Indie Hackers, and Marketers.
Stop wasting time staring at a blank cursor. Growth Hacker MCP gives your AI agent the tools to write viral threads, format them perfectly, and audit your sales copy like a pro copywriter.
generate_hooksInstantly generate 5+ viral hook templates for any topic.
Input: "AI Agents", "SaaS Sales", "Gardening"
Output: Proven viral structures ready to fill in.
format_threadTurn any wall of text into a perfectly formatted X (Twitter) thread.
Automatically splits into <280 char tweets.
Adds numbering (1/x).
Preserves flow.
audit_copyGet a "Copy Score" and actionable advice for your Gumroad or Landing Page.
Checks for "Power Words".
Analyzes length and readability.
Reminds you of missing CTAs.
Related MCP server: blogburst-mcp-server
npm install
npm run buildAdd this to your claude_desktop_config.json:
{
"mcpServers": {
"growth-hacker": {
"command": "node",
"args": ["/path/to/growth-hacker-mcp/build/index.js"]
}
}
}npm install
npm run devMIT
Audit sales copy for Gumroad/Landing pages and give a score.
| Name | Required | Description | Default |
|---|---|---|---|
| copy | Yes | The sales copy text |
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 states the tool audits and scores copy, but doesn't explain what 'audit' entails (e.g., criteria, metrics), how the score is calculated, or any limitations (e.g., length constraints, language support). This leaves significant gaps in understanding the tool's behavior.
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, efficient sentence that directly states the tool's purpose without unnecessary words. It's front-loaded with the core action and context, making it easy to parse quickly.
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 no annotations and no output schema, the description is incomplete. It doesn't explain what the audit evaluates, how the score is presented, or any behavioral traits like error handling. Given the complexity of auditing and scoring text, more context is needed to use this tool effectively.
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 has 100% description coverage, with the 'copy' parameter clearly documented as 'The sales copy text.' The description adds no additional semantic details beyond this, such as format examples or content guidelines. Given the high schema coverage, a baseline score of 3 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 clearly states the tool's purpose: 'Audit sales copy for Gumroad/Landing pages and give a score.' It specifies the action (audit), resource (sales copy), and context (Gumroad/Landing pages). However, it doesn't explicitly differentiate from sibling tools like 'format_thread' or 'generate_hooks', which prevents a perfect score.
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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any prerequisites, exclusions, or compare it to sibling tools like 'format_thread' or 'generate_hooks', leaving the agent with minimal context for selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Split long text into a numbered X (Twitter) thread (1/x).
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | The full text content |
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 describes the basic behavior (splitting text into a thread) but lacks details on constraints like character limits per thread part, handling of formatting or media, error conditions, or rate limits. This is a significant gap for a tool that modifies text output.
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, efficient sentence that front-loads the core purpose without unnecessary words. It directly communicates what the tool does, making it easy to understand at a glance.
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 the complexity of text processing and lack of annotations or output schema, the description is incomplete. It doesn't explain the thread format details (e.g., numbering style, length limits), return values, or error handling. This leaves gaps for an AI agent to use the tool effectively.
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 has 100% description coverage, with one parameter 'text' documented as 'The full text content.' The description adds no additional meaning beyond this, such as examples or formatting requirements. Baseline 3 is appropriate since the schema handles the parameter documentation adequately.
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's purpose: 'Split long text into a numbered X (Twitter) thread (1/x).' It specifies the action (split), the resource (long text), and the output format (numbered X thread). However, it doesn't differentiate from sibling tools like 'audit_copy' or 'generate_hooks', which appear unrelated but no explicit distinction is made.
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 provides no guidance on when to use this tool versus alternatives. It mentions the output format but doesn't specify scenarios, prerequisites, or exclusions. Without context on sibling tools, users must infer usage based on the purpose alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Generate viral hooks for X (Twitter) threads based on a topic.
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | The topic to generate hooks for |
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 generates content but doesn't cover aspects like whether it requires authentication, rate limits, output format, or potential side effects (e.g., if it posts to Twitter or just creates drafts). This leaves significant gaps for a tool that likely involves AI generation.
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, efficient sentence that front-loads the core action ('Generate viral hooks') and specifies the context ('for X (Twitter) threads based on a topic'). There is no wasted verbiage, making it highly concise and well-structured for quick understanding.
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 the complexity of content generation, lack of annotations, and no output schema, the description is incomplete. It doesn't explain what 'viral hooks' entail, the output format (e.g., text list, structured data), or any behavioral traits like error handling. This leaves the agent with insufficient context to use the tool effectively beyond basic 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 description coverage is 100%, with the single parameter 'topic' well-documented in the schema. The description adds no additional parameter semantics beyond implying the topic is used for hook generation, which doesn't enhance the schema's information. Baseline 3 is appropriate as the schema handles the parameter documentation adequately.
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 action ('generate viral hooks') and target resource ('for X (Twitter) threads'), with a specific purpose ('based on a topic'). It doesn't differentiate from sibling tools like 'audit_copy' or 'format_thread', which prevents a perfect score, but the verb+resource combination is unambiguous.
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 is provided on when to use this tool versus alternatives like 'format_thread' or other content generation methods. The description implies usage for creating hooks on Twitter threads but lacks explicit context, prerequisites, or exclusions, leaving the agent to infer appropriate scenarios.
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.
audit_copyformat_threadgenerate_hooksEach tool has a clearly distinct purpose: auditing sales copy, formatting text into threads, and generating viral hooks. There is no overlap in functionality, making it easy for an agent to select the right tool without confusion.
All tool names follow a consistent verb_noun pattern (audit_copy, format_thread, generate_hooks), using snake_case throughout. This predictability enhances readability and usability.
With only 3 tools, the set feels thin for a 'Growth Hacker' domain, which typically involves broader tasks like analytics, A/B testing, or campaign management. While the tools are focused, the count may limit comprehensive growth hacking workflows.
The toolset is severely incomplete for growth hacking, missing core operations such as data analysis, user engagement tracking, or performance optimization. It covers only copy and social media aspects, leaving significant gaps that could hinder agent effectiveness.
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If you are the server author, to access and configure the admin panel.
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