Extract clean, readable content from any URL. Returns markdown text, links, and metadata. No API keys, no config. A free alternative to Firecrawl for scraping docs, blogs, and articles.
npx mcp-server-scraperWorks with Claude Desktop, Cursor, VS Code Copilot, and any MCP client. No accounts or API keys needed.
Demo built with remotion-readme-kit
When you're working with an AI assistant and need to reference a docs page, a blog post, or an API reference, you usually end up copy-pasting content manually. Tools like Firecrawl solve this but require a paid API key. This server does the same thing for free. It fetches a URL, runs it through Mozilla Readability (the same engine behind Firefox Reader View), and returns clean markdown. It works well for server-rendered content like documentation sites, blog posts, and articles. It won't handle JavaScript-heavy SPAs, but for the most common use case of "read this docs page and summarize it," it does the job.
Related MCP server: @hauntapi/mcp-server
Tool | What it does |
| Extract clean text content from a URL (Readability-powered) |
| Get all links with href and anchor text |
| Get title, description, OG tags, canonical, favicon |
| Search for a query string within the page, return matching lines |
| Batch scrape multiple URLs, get title + excerpt per URL |
Add to .cursor/mcp.json:
{
"mcpServers": {
"scraper": {
"command": "npx",
"args": ["-y", "mcp-server-scraper"]
}
}
}Add to claude_desktop_config.json:
{
"mcpServers": {
"scraper": {
"command": "npx",
"args": ["-y", "mcp-server-scraper"]
}
}
}Add to your MCP settings (e.g. .vscode/mcp.json):
{
"mcp": {
"servers": {
"scraper": {
"command": "npx",
"args": ["-y", "mcp-server-scraper"]
}
}
}
}"Scrape the API docs from https://docs.example.com and summarize them"
"Extract all links from this page"
"What's the OG image and description for this URL?"
"Search this page for mentions of 'authentication'"
"Scrape these 5 URLs and give me a summary of each"
Uses Mozilla Readability (the engine behind Firefox Reader View) plus linkedom for fast HTML parsing in Node. No headless browser needed. Works best with server-rendered pages: docs, blogs, articles, news sites.
This repo is an Agent Plugins 1.0.0 package: plugin.json, portable mcp.json, and skills/ ship together with the MCP server.
For Cursor, clone the repo and copy or symlink it to ~/.cursor/plugins/local/mcp-server-scraper, then reload the window. Skills and MCP show up under Customize > Plugins.
The Cursor and VS Code install buttons above still work: they add the same npx -y mcp-server-scraper stdio server as manual JSON.
A free MCP server that turns public web pages into clean markdown using Mozilla Readability. No Firecrawl or other scrape API key.
No. It fetches HTML and parses it in Node. Use a browser MCP for React dashboards and other client-rendered sites.
Firecrawl is a hosted scrape API with billing. This server runs locally via npx, costs nothing, and fits doc/blog/article URLs.
Yes. Use the local plugin path under ~/.cursor/plugins/local/mcp-server-scraper so the bundled web-scraping skill loads with the MCP config.
No. Point your MCP client at npx -y mcp-server-scraper only.
npm install
npm run typecheck
npm run build
npm testMore MCP servers and developer tools on my portfolio.
README built with README Builder
Extract all links from a page with their href and anchor text. Resolves relative URLs. Skips anchors and javascript: links.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to extract links from |
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 effectively describes key behaviors: it extracts all links (comprehensive), includes href and anchor text (output format), resolves relative URLs (transformation behavior), and skips anchors and javascript: links (filtering behavior). This provides good transparency about what the tool does beyond basic extraction.
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 extremely concise (one sentence) and front-loaded with all essential information. Every element earns its place: what it extracts, what attributes it includes, URL resolution behavior, and what it excludes. There's zero wasted text.
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 single-parameter tool with no annotations and no output schema, the description provides excellent coverage of what the tool does, how it behaves, and what it returns. The main gap is the lack of explicit output format details (though 'href and anchor text' gives some indication), but given the tool's simplicity and the description's clarity, it's nearly complete.
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 a single parameter 'url' clearly documented as 'The URL to extract links from'. The description doesn't add any additional parameter semantics beyond what the schema provides, but with complete schema coverage, the baseline score of 3 is appropriate 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the specific action ('extract all links'), resource ('from a page'), and scope ('with their href and anchor text'). It distinguishes from siblings like extract_metadata (which likely extracts different data) and scrape_url/scrape_multiple (which might return full page content rather than just links).
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 clear context about what the tool does (extracts links with specific attributes and resolves relative URLs), which helps differentiate it from siblings. However, it doesn't explicitly state when to use this tool versus alternatives like search_page or scrape_url, nor does it mention any exclusions or prerequisites for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract page metadata: title, description, Open Graph tags (og:title, og:description, og:image), canonical URL, and favicon.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to extract metadata from |
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 states what metadata is extracted but doesn't disclose behavioral traits such as error handling, rate limits, authentication needs, or what happens if metadata is missing. This is a significant gap for a tool with no 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that front-loads the purpose and lists specific metadata types. There is zero waste, and 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?
Given no annotations, no output schema, and a simple input schema, the description is adequate for a basic extraction tool but lacks completeness. It doesn't explain return values, error cases, or behavioral context, which are needed for full understanding.
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%, so the schema already documents the 'url' parameter. The description adds no additional parameter semantics beyond what the schema provides, such as URL format expectations or examples. Baseline 3 is appropriate when schema does the heavy lifting.
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 verb 'extract' and the resource 'page metadata', with specific examples of what metadata is extracted (title, description, Open Graph tags, canonical URL, favicon). It distinguishes from sibling tools like 'extract_links' by focusing on metadata rather than links.
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 'scrape_url' or 'search_page'. The description implies usage for extracting metadata from a URL but doesn't specify scenarios, prerequisites, or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Batch scrape multiple URLs. Returns title and excerpt for each. Failures are reported per URL without failing the whole batch.
| Name | Required | Description | Default |
|---|---|---|---|
| urls | Yes | Array of URLs to scrape |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does well by disclosing key behavioral traits: it's a batch operation, returns specific data (title and excerpt), and has fault-tolerant error handling. It doesn't mention rate limits, authentication needs, or performance characteristics, but covers the essential behavior adequately for a read-only scraping 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?
The description is perfectly concise with two sentences that each earn their place: the first states the core functionality and output, the second explains the error handling behavior. No wasted words, front-loaded with the most important information.
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 tool's moderate complexity (batch scraping with partial failure tolerance), no annotations, and no output schema, the description provides good context about what the tool does and how it behaves. It could be more complete by specifying output format details or limitations, but covers the essential aspects well for an agent to use it correctly.
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 'urls' parameter fully documented as 'Array of URLs to scrape'. The description adds no additional parameter semantics beyond what the schema provides, but doesn't need to since the schema is complete. This meets the baseline of 3 for high schema coverage.
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 specific action ('Batch scrape multiple URLs') and the resource ('URLs'), distinguishing it from siblings like 'scrape_url' (single URL) and 'extract_links'/'extract_metadata' (different extraction types). It explicitly mentions what it returns ('title and excerpt for each'), making the purpose 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?
The description provides clear context for when to use this tool: for batch processing of URLs where partial failures are acceptable ('Failures are reported per URL without failing the whole batch'). However, it doesn't explicitly mention when NOT to use it or name alternatives like 'scrape_url' for single URLs, which would have earned a 5.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Extract clean, readable text content from a URL using Mozilla Readability. Returns title, excerpt, and main content. Best for articles, docs, and blog posts.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to scrape |
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 extraction method ('using Mozilla Readability') and return format, but lacks details on error handling, rate limits, authentication needs, or performance characteristics. It adequately describes the core behavior but misses deeper 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 front-loaded with the core purpose, followed by return values and usage context in just two sentences. Every sentence adds value without redundancy, making it highly efficient 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 tool's moderate complexity (single parameter, no output schema, no annotations), the description is fairly complete: it covers purpose, method, returns, and usage context. However, it could improve by addressing potential limitations (e.g., handling non-article pages) or output structure details, slightly reducing 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?
The input schema has 100% description coverage, with the 'url' parameter fully documented in the schema itself. The description doesn't add any parameter-specific details beyond what the schema provides, such as URL format constraints or examples. 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.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose with specific verbs ('Extract clean, readable text content') and resources ('from a URL using Mozilla Readability'), distinguishing it from siblings like extract_links (which extracts links) and extract_metadata (which extracts metadata). It also specifies the return values (title, excerpt, main content) and ideal use cases (articles, docs, blog posts).
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 clear context on when to use this tool ('Best for articles, docs, and blog posts'), which helps differentiate it from siblings like scrape_multiple (for multiple URLs) or search_page (which might involve searching). However, it doesn't explicitly state when NOT to use it or name specific alternatives, keeping it from a perfect score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search for a query string within the page text. Returns matching lines (one per line). Use for finding mentions of a term.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to search | |
| query | Yes | The search query |
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. While it mentions the return format ('matching lines (one per line)'), it doesn't address important behavioral aspects like whether this is a read-only operation, potential rate limits, authentication requirements, error conditions, or how it handles large pages. For a search tool with zero annotation coverage, this leaves significant gaps.
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 perfectly concise with two sentences that each earn their place. The first sentence states the core functionality, and the second provides usage context and return format. There's zero wasted text, and the information is front-loaded appropriately.
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 tool's moderate complexity (search operation with 2 parameters), no annotations, and no output schema, the description provides basic but incomplete coverage. It explains what the tool does and the return format, but lacks details about behavioral constraints, error handling, and output structure that would be important for an AI agent to use it 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?
Schema description coverage is 100%, so the input schema already documents both parameters (url and query) with their types and requirements. The description doesn't add any parameter-specific information beyond what's in the schema, such as query syntax, URL validation rules, or search scope details. Baseline 3 is appropriate when the schema does the heavy lifting.
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 with specific verbs ('search for', 'returns matching lines') and resource ('within the page text'). It distinguishes from sibling tools like extract_links, extract_metadata, scrape_multiple, and scrape_url by focusing specifically on text search rather than extraction or scraping operations.
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 clear context for when to use this tool ('for finding mentions of a term'), which implicitly differentiates it from siblings that handle link extraction, metadata extraction, or general scraping. However, it doesn't explicitly state when NOT to use this tool or name specific alternatives among the siblings.
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.
v1.0.0extract_linksextract_metadatascrape_multiplescrape_urlsearch_pageEach tool has a clearly distinct purpose with no overlap: extract_links focuses on hyperlinks, extract_metadata on page metadata, scrape_multiple on batch title/excerpt extraction, scrape_url on full content extraction, and search_page on text search. The descriptions make it easy to differentiate them, preventing misselection.
All tool names follow a consistent verb_noun pattern with snake_case (e.g., extract_links, scrape_url, search_page). The naming is predictable and readable throughout, with no deviations in style or convention.
With 5 tools, this server is well-scoped for web scraping purposes. Each tool earns its place by covering distinct aspects of scraping (links, metadata, batch processing, content extraction, and search), avoiding bloat while providing comprehensive functionality.
The tool set covers core web scraping workflows effectively, including extraction, metadata, batch operations, and search. A minor gap exists in lacking explicit update or delete operations, but these are not typical for scraping tasks, and agents can work around this with the provided tools.
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If you are the server author, to access and configure the admin panel.
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Cloud scraping & crawling API for AI agents. Turn any URL into clean, LLM-ready markdown.
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