Encuentra enlaces de afiliados rotos rápidamente, priorízalos por impacto y genera sugerencias de reparación que tu agente de IA pueda ejecutar.
Una llamada. Más de 38 redes de afiliados verificadas. Pérdida de ingresos estimada.
Instalación con un clic: Instalar en MCPize |
pip install linkrescue-mcp
LinkRescue MCP expone flujos de trabajo de escaneo, monitorización y remediación de enlaces rotos a través del Protocolo de Contexto de Modelo (MCP), para que herramientas como Claude y Cursor puedan ejecutar operaciones de salud de enlaces directamente.
check_broken_links: escanea una URL (o sitemap) y devuelve un informe estructurado de enlaces rotos
monitor_links: configura una monitorización recurrente para un sitio web
get_fix_suggestions: genera recomendaciones de remediación priorizadas
health_check: verifica la conectividad del servidor MCP y la API de backend
Si la API de backend de LinkRescue no está disponible, el servidor recurre a datos simulados realistas para que las pruebas locales y las demostraciones sigan funcionando.
Related MCP server: webcheck-mcp
{
"mcpServers": {
"linkrescue": {
"command": "linkrescue-mcp"
}
}
}Luego pregúntale a tu agente de IA:
"Escanea example.com en busca de enlaces de afiliados rotos"
Herramienta | Gratis | Pro ($19/mes) | Agencia ($29/mes) |
| Sí | Sí | Sí |
| Sí | Sí | Sí |
| - | Sí | Sí |
| - | - | Sí |
| - | Sí | Sí |
| - | Sí | Sí |
| - | - | Sí |
Estimaciones de pérdida de ingresos | - | Sí | Sí |
Monitorización de múltiples sitios | - | 5 sitios | 25 sitios |
El nivel gratuito te ofrece comprobaciones de enlaces rotos de una sola página. Pro desbloquea el rastreador completo + sugerencias de reparación + monitorización recurrente. Agencia añade comprobaciones por hora, webhooks y un número ilimitado de sitios.
Actualiza a Pro en MCPize — $19/mes o $190/año. Agencia $29/mes o $290/año.
Instalación con un clic con alojamiento gestionado: Instalar en MCPize
pip install linkrescue-mcp
linkrescue-mcpgit clone https://github.com/carsonroell-debug/linkrescue-mcp.git
cd linkrescue-mcp
pip install -r requirements.txt
python main.pyEndpoint MCP:
http://localhost:8000/mcp
Variable | Descripción | Predeterminado |
| URL base para la API de LinkRescue |
|
| Clave API para solicitudes autenticadas | vacío |
Ejemplo:
export LINKRESCUE_API_BASE_URL="https://www.linkrescue.io/api/v1"
export LINKRESCUE_API_KEY="your-api-key"
linkrescue-mcpObtén una clave API en linkrescue.io/settings/api (solo niveles Pro y Agencia).
Ejecutar a través del punto de entrada instalado:
linkrescue-mcpEjecutar directamente desde el código fuente:
python main.pyEjecutar a través de la CLI de FastMCP:
fastmcp run main.py --transport streamable-http --port 8000Añade esto a claude_desktop_config.json:
{
"mcpServers": {
"linkrescue": {
"command": "linkrescue-mcp"
}
}
}claude mcp add linkrescue --transport http http://localhost:8000/mcpfastmcp list-tools main.py
fastmcp call-tool main.py health_check '{}'
fastmcp call-tool main.py check_broken_links '{"url":"https://example.com"}'check_broken_linksEntradas:
url (obligatorio): URL del sitio a escanear
sitemap_url (opcional, nivel Agencia): rastrear desde sitemap
max_depth (opcional, predeterminado 3): profundidad de rastreo
Devuelve metadatos del escaneo, detalles de enlaces rotos y estadísticas resumidas. Los niveles Pro y Agencia incluyen la pérdida de ingresos mensual estimada por enlaces de afiliados rotos.
monitor_linksEntradas:
url (obligatorio)
frequency_hours (opcional, predeterminado 24; el nivel Agencia admite 1)
Devuelve el ID de monitorización, detalles de programación y estado. El nivel gratuito devuelve un monitor simulado (sin persistencia).
get_fix_suggestionsEntrada:
informe completo de check_broken_links, o
array sin procesar de broken_links, o
cadena JSON de cualquiera de los formatos
Devuelve acciones priorizadas y pasos de remediación sugeridos. Solo niveles Pro y Agencia.
health_checkSin entrada. Devuelve el estado del servidor y la accesibilidad de la API de backend.
Este repositorio incluye smithery.yaml y smithery.json.
Envía el repositorio a GitHub
Crea/añade el servidor en Smithery
Apunta Smithery a este repositorio
Se incluye un Dockerfile para Railway, Fly.io y otros hosts de contenedores.
# Railway
railway up
# Fly.io
fly launch
fly deployEstablece LINKRESCUE_API_BASE_URL y LINKRESCUE_API_KEY en el entorno de tu host.
Agent (Claude, Cursor, etc.)
-> MCP
LinkRescue MCP Server (this repo)
-> HTTP API
LinkRescue Backend API (linkrescue.io)Este servidor es una capa de traducción entre las llamadas a herramientas MCP y las operaciones de la API de LinkRescue.
MIT — Creado por Freedom Engineers
SelfHeal MCP — Proxy de autocuración para servidores MCP
SiteHealth MCP — Auditoría completa de salud del sitio web
LeadEnrich MCP — Enriquecimiento de leads en cascada
Versión enfocada en desarrolladores: README.dev.md
Versión enfocada en el mercado: README.marketplace.md
Scans a single URL or entire site/sitemap for broken links.
Returns a structured report with every broken link found, its HTTP status code, the page it was discovered on, link type (affiliate/external/internal), SEO impact rating, and estimated revenue loss.
Agents can pass the output directly to get_fix_suggestions for remediation steps.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The website URL to scan (e.g. "https://example.com"). | |
| sitemap_url | No | Optional sitemap URL to crawl instead of discovering pages by depth. | |
| max_depth | No | How many levels deep to crawl from the start URL. Default 3. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 describes the output format well ('structured report with every broken link found...') and mentions integration with another tool. However, it doesn't cover important behavioral aspects like rate limits, authentication needs, execution time, or error handling for a scanning tool that could be resource-intensive.
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 structured and concise - three sentences that each earn their place. The first states the purpose, the second details the output, and the third provides integration guidance. No wasted words, front-loaded with the core functionality.
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 has an output schema (mentioned in context signals), the description doesn't need to explain return values in detail. It provides good context about the scanning scope and output integration. However, for a scanning tool with no annotations, it could better address behavioral aspects like performance characteristics or limitations.
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 fully documents all 3 parameters. The description adds no additional parameter information beyond what's in the schema. The baseline score of 3 is appropriate when the schema does all the parameter documentation work.
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 ('scans', 'returns') and resources ('URL or entire site/sitemap', 'broken links'). It distinguishes from siblings by mentioning the specific output format and direct integration with get_fix_suggestions, which differentiates it from health_check and monitor_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 when to use this tool ('scans a single URL or entire site/sitemap for broken links') and mentions integration with get_fix_suggestions for remediation. However, it doesn't explicitly state when NOT to use it or provide alternatives among siblings like health_check or monitor_links.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Given a broken links report, returns prioritized remediation suggestions.
Each suggestion includes the broken URL, a recommended action (update link, follow redirect, remove, etc.), a human-readable explanation, and a code snippet where applicable.
Accepts either the full JSON report from check_broken_links or just the broken_links array.
| Name | Required | Description | Default |
|---|---|---|---|
| broken_links_report | Yes | The scan report (JSON string or dict) from check_broken_links. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden. It discloses the tool's output format (prioritized suggestions with specific fields) and input flexibility (accepts full JSON or just array). However, it doesn't mention performance characteristics, error handling, or whether this is a read-only operation (though implied by 'returns').
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 front-loaded with the core purpose in the first sentence, followed by details about output format and input flexibility. Every sentence adds value with zero waste. The structure flows logically from purpose to output details to input requirements.
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 has an output schema (which covers return values), no annotations, and 100% schema coverage, the description provides good context about purpose, usage, and behavioral aspects. However, for a tool that processes potentially complex broken link data, more detail about prioritization logic or suggestion criteria would enhance 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?
Schema description coverage is 100%, so the baseline is 3. The description adds meaningful context by explaining the parameter accepts either 'the full JSON report from check_broken_links or just the broken_links array' - clarifying format flexibility beyond what the schema's 'anyOf' indicates. This elevates the score above baseline.
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 ('returns prioritized remediation suggestions') and resources ('broken links report'). It distinguishes from sibling tools by specifying it processes output from 'check_broken_links' rather than performing scanning or monitoring itself.
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 ('Given a broken links report') and mentions the specific sibling tool 'check_broken_links' as the source. However, it doesn't explicitly state when NOT to use it or mention alternatives among the other siblings like 'health_check' or 'monitor_links'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Confirms the LinkRescue MCP server is alive and can reach the backend API.
Use this to verify connectivity before running scans. Returns server status, API reachability, and current configuration.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
| Name | Required | Description |
|---|---|---|
No output parameters | ||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing what the tool returns ('server status, API reachability, and current configuration') and its verification purpose. It doesn't mention error behavior or performance characteristics, but provides solid 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?
Perfectly structured with two sentences: first states purpose, second provides usage guidance and return values. Every word earns its place with zero redundancy or 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?
Complete for a zero-parameter health check tool with output schema. The description explains what it does, when to use it, and what it returns - covering all necessary context given the tool's simplicity and available structured data.
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 tool has zero parameters with 100% schema coverage, so baseline is 4. The description appropriately doesn't discuss parameters since none exist, focusing instead on the tool's purpose and output.
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 ('confirms', 'verify connectivity') and resources ('LinkRescue MCP server', 'backend API'). It distinguishes from siblings by focusing on server health rather than link scanning or monitoring 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?
Explicitly states when to use this tool ('before running scans') and provides clear context about its verification purpose. The description distinguishes it from sibling tools that perform actual link operations rather than connectivity checks.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Sets up ongoing broken-link monitoring for a website.
The monitor runs on a schedule (default: every 24 hours) and will detect new broken links, links that were fixed, and changes in site health score.
Returns a monitoring_id you can reference later to check status or cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | The website URL to monitor. | |
| frequency_hours | No | How often to re-scan, in hours. Default 24. |
| Name | Required | Description |
|---|---|---|
No output parameters | ||
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 setup/creation tool (implies mutation), runs on a schedule with a default frequency, detects specific changes (new broken links, fixed links, health score changes), and returns a monitoring_id for future reference. It doesn't mention permissions, rate limits, or error handling, but covers core behavior adequately.
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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by details on schedule and detection scope, ending with return value. Every sentence adds value with zero waste, 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 tool's moderate complexity (setup of ongoing monitoring), no annotations, 100% schema coverage, and the presence of an output schema (implied by 'Returns a monitoring_id'), the description is complete enough. It explains what the tool does, how it behaves, and the return value, leaving schema details to structured fields.
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 fully documents both parameters (url and frequency_hours). The description adds no additional parameter semantics beyond what's in the schema, such as URL format constraints or frequency limits. 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 tool's purpose with specific verbs ('Sets up ongoing broken-link monitoring') and identifies the resource ('for a website'). It distinguishes from sibling tools like 'check_broken_links' (one-time check) by emphasizing ongoing monitoring on a schedule.
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 (ongoing monitoring vs. one-time checks) and implies alternatives through sibling tool names like 'check_broken_links'. However, it doesn't explicitly state when NOT to use it or directly compare to all 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.
v0.1.1check_broken_linksget_fix_suggestionshealth_checkmonitor_linksScored across 4 tools
Each tool has a clearly distinct purpose: check_broken_links for scanning, get_fix_suggestions for remediation, health_check for connectivity, and monitor_links for ongoing monitoring. There is no overlap or ambiguity in their functions.
The tools follow a consistent verb_noun pattern (check_broken_links, get_fix_suggestions, monitor_links), with one minor deviation (health_check uses noun_verb). This is mostly consistent and readable.
With 4 tools, the server is well-scoped for its purpose of broken link detection and management. Each tool earns its place, covering scanning, remediation, monitoring, and health checks without being too sparse or bloated.
The tool set provides complete coverage for the domain: check_broken_links for detection, get_fix_suggestions for remediation, monitor_links for ongoing tracking, and health_check for connectivity. There are no obvious gaps, and agents can follow a full workflow from scan to fix to monitoring.
Security, SEO and AI-visibility scanner for web apps · free scans and focused checks via MCP.
SEO MCP server: crawl your site, find AI-visibility gaps, and ship the fix from your coding agent.
Crawl a site for broken links, 404s, dead images, redirect chains and slow pages, with sources
Monitor MCP servers, API contracts and AI outputs for schema drift. Alerts on breaking changes.
