Fetch Browser
Obtener navegador
Un potente servidor MCP de navegador sin interfaz gráfica que permite a los agentes de IA obtener contenido web y realizar búsquedas en Google sin necesidad de claves API. Desarrollado con estándares web modernos y diseñado para una integración fluida con plataformas de IA.
Características
No se requieren claves API : utiliza un sofisticado enfoque de navegador sin cabeza para obtener contenido directamente
Búsqueda inteligente de Google :
Extraer resultados estructurados de las búsquedas de Google
Soporte para resultados web y de noticias
Número configurable de resultados (1-100)
Obtención de contenido completo de los resultados de búsqueda
Reintento automático y manejo de errores
Obtención de contenido universal :
Soporte para cualquier página web o punto final de API
Múltiples formatos de salida (HTML, JSON, Texto, Markdown)
Detección automática del tipo de contenido
Límites de tamaño de respuesta y protección de tiempo de espera
Protección de limitación de velocidad
Conversión de formato :
Conversión limpia de HTML a Markdown
Respuestas JSON con formato bonito
Salida HTML estructurada
Texto simple con formato adecuado
Related MCP server: Local Browser MCP
Herramientas
1. Herramienta de búsqueda de Google ( google_search )
Ejecuta búsquedas de Google y obtiene el contenido completo de los resultados:
{
query: "your search query",
responseType: "markdown", // or "json", "html", "text"
maxResults: 10, // 1-100 results
topic: "web" // or "news"
}2. Herramienta de obtención de URL ( fetch_url )
Obtiene contenido de cualquier URL:
{
url: "https://example.com",
responseType: "markdown", // or "json", "html", "text"
timeout: 30000 // optional, in milliseconds
}Instalación
Vía Smithery
# For Claude
npx -y @smithery/cli install @TheSethRose/fetch-browser --client claude
# For Cursor
npx -y @smithery/cli install @TheSethRose/fetch-browser --client cursor
# For TypeScript
npx -y @smithery/cli install @TheSethRose/fetch-browser --client typescriptInstalación manual
# Clone the repository
git clone https://github.com/TheSethRose/fetch-browser.git
cd fetch-browser
# Install dependencies
npm install
# Build the project
npm run build
# Start the server
npm startDesarrollo
# Watch mode with auto-rebuild
npm run watch
# Run with inspector
npm run inspector
# Debug mode
npm run debug
# Debug with watch mode
npm run debug:watchFormatos de respuesta
1. Formato Markdown
## [Page Title](https://example.com)
Content converted to clean markdown with:
- Lists
- **Bold text**
- *Italic text*
- [Links](https://example.com)2. Formato JSON
{
"url": "https://example.com",
"content": "Extracted content...",
"error": null
}3. Formato HTML
<div class="search-result">
<h2><a href="https://example.com">Page Title</a></h2>
<div class="content">
Original HTML content
</div>
</div>4. Formato del texto
### https://example.com
Plain text content with preserved formatting
==========Manejo de errores
Reintentos automáticos con retroceso exponencial
Protección de limitación de velocidad
Manejo del tiempo de espera
Mensajes de error detallados
Seguimiento de errores de resultados individuales
Características de seguridad
Límites de tamaño de respuesta
Tiempos de espera de solicitud
Protección de limitación de velocidad
No se requieren claves API ni secretos
Manejo adecuado de errores
Créditos
Creado por Seth Rose :
Sitio web : https://www.sethrose.dev
𝕏 (Twitter) : https://x.com/TheSethRose
🦋 (Cielo azul) : https://bsky.app/profile/sethrose.dev
Licencia
Licencia MIT: consulte LICENCIA para obtener más detalles
Available Tools
2 toolsfetch_urlC
Fetch content from a URL with proper error handling and response processing
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The URL to fetch | |
| responseType | No | Expected response type | text |
| timeout | No | Request timeout in milliseconds |
TDQS
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 'proper error handling and response processing', which hints at robustness, but lacks specifics on authentication needs, rate limits, retry behavior, or what constitutes 'proper' processing. This is insufficient for a tool that interacts with external URLs.
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. However, it could be more structured by separating purpose from behavioral claims, and the phrase 'proper error handling and response processing' is somewhat vague and could be tightened.
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 fetching from URLs (potential for errors, varied content types) and the absence of annotations and output schema, the description is incomplete. It doesn't cover return values, error formats, or detailed behavioral traits needed for reliable use by an AI agent.
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 all parameters thoroughly. The description adds no additional meaning about parameters beyond what's in the schema, such as explaining the implications of different response types or timeout values. 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 action ('fetch content from a URL') and resource ('URL'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from the sibling tool 'google_search', which likely serves a different purpose (searching vs. direct fetching).
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 like 'google_search'. It mentions 'proper error handling and response processing' but doesn't specify scenarios, prerequisites, or exclusions for usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
google_searchC
Execute a Google search and return results in various formats
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | The search query to execute | |
| responseType | No | Expected response type | json |
| maxResults | No | Maximum number of results to return | |
| topic | No | Type of search to perform | web |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but only mentions the action and output formats. It fails to disclose critical behavioral traits such as rate limits, authentication needs, network dependencies, or error handling. The description is minimal and doesn't compensate for the lack of 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, efficient sentence that front-loads the core action and outcome with zero waste. Every word earns its place, 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 tool's complexity (search with multiple parameters) and lack of annotations and output schema, the description is incomplete. It doesn't explain result formats, error cases, or operational constraints, leaving significant 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?
Schema description coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning beyond what's in the schema, such as explaining the impact of 'responseType' choices or 'topic' differences. Baseline 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 verb ('execute') and resource ('Google search') with the outcome ('return results in various formats'). It distinguishes from the sibling 'fetch_url' by focusing on search rather than URL retrieval. However, it doesn't specify what 'various formats' means beyond the schema's enum values, keeping it from 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 like 'fetch_url' or other search methods. It lacks context about use cases, prerequisites, or exclusions, leaving the agent to infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
2 tool updates
- First observed
fetch_url - First observed
google_search
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: fetch_url retrieves content from a specific URL, while google_search performs web searches and returns results. There is no overlap in functionality, making it easy for an agent to choose the correct tool based on the task.
Both tools follow a verb_noun pattern (fetch_url, google_search), which is consistent and predictable. The minor deviation is that google_search includes a brand name, but this does not break the overall naming convention.
With only 2 tools, the server feels thin for a browser-related purpose, as it lacks common operations like navigating pages, handling cookies, or interacting with web elements. However, the tools provided are core functionalities, so it's borderline appropriate.
For a browser server, there are significant gaps in coverage, such as no tools for page navigation, form submission, JavaScript execution, or session management. The surface is severely incomplete for typical browser automation tasks, limiting agent effectiveness.
Maintenance
Related MCP Connectors
Official Browserless MCP server: browser automation, scraping, screenshots and PDFs for AI agents.
Free web search for AI agents. No API key required. Hosted MCP in active development.
Docs: https://docs.keenable.ai/mcp-server Keenable is a free, remote MCP server that gives agents access to the web index. Search the web with ranked results and date/site filters, then fetch any indexed page as clean markdown. Works out of the box with no account or API key.
Turn any public website into an MCP server for agents to search, read and navigate.
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