Downloads and parses Substack posts, extracting title, author, subtitle, and content from public posts.
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., "@MCP Substack Server download and summarize this Substack post: https://example.substack.com/p/post-title"
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.
A Model Context Protocol (MCP) server for downloading and parsing Substack posts. Works with Claude.ai desktop app.
Install dependencies:
npm installConfigure Claude desktop app:
Add to ~/Library/Application Support/Claude/claude_desktop_config.json:
{
"mcpServers": {
"mcp-substack": {
"command": "/opt/homebrew/bin/node",
"args": ["/path/to/mcp-substack/lib/index.mjs"],
"methods": {
"download_substack": {
"description": "Download and parse content from a Substack post"
}
}
}
}
}Start the server:
npm startRelated MCP server: Substack MCP
In Claude desktop app, use:
Could you download and summarize this Substack post: [URL]Downloads and parses Substack posts
Extracts title, author, subtitle, and content
Works with public Substack posts
Integrates with Claude.ai desktop app
Node.js v18+
Claude desktop app
MIT
Download and parse content from a Substack post
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | URL of the Substack post |
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 'download and parse,' implying network access and content processing, but fails to detail critical aspects like error handling, rate limits, authentication needs, or output format. For a tool with no annotation coverage, this leaves the agent with insufficient operational context.
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 functionality without any wasted words. It directly communicates the tool's purpose in a clear and structured manner, making it easy for an agent 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?
Given the absence of annotations and output schema, the description is incomplete for effective tool use. It lacks details on behavioral traits (e.g., network behavior, error cases) and output specifics, which are crucial for an agent to invoke the tool correctly. While the purpose is clear, the overall context is insufficient for a tool that performs content retrieval and parsing.
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 schema description coverage is 100%, with the single parameter 'url' fully documented in the schema. The description does not add any meaning beyond what the schema provides, such as URL format examples or validation rules. However, with high schema coverage, the baseline score of 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 ('download and parse') and resource ('content from a Substack post'), making the purpose immediately understandable. However, since there are no sibling tools, it cannot differentiate from alternatives, which prevents a perfect score. The description avoids tautology by specifying the action beyond just the tool name.
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, prerequisites, or exclusions. It simply states what the tool does without context for application. With no sibling tools, this is less critical, but the absence of any usage context still represents a significant gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool's purpose is clearly defined and distinct by default.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.
One tool is too few for a server named 'MCP Substack Server', which implies broader Substack functionality. It feels thin and under-scoped, lacking operations like listing posts, subscribing, or managing content beyond downloading.
The tool surface is severely incomplete for a Substack domain. It only supports downloading posts, missing core operations such as creating, updating, deleting posts, or interacting with subscribers, comments, or analytics.
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