Быстро находите неработающие партнерские ссылки, расставляйте приоритеты по уровню влияния и генерируйте предложения по исправлению, которые может выполнить ваш ИИ-агент.
Один вызов. Проверка более 38 партнерских сетей. Оценка потери дохода.
Установка в один клик: Установить через MCPize |
pip install linkrescue-mcp
LinkRescue MCP предоставляет рабочие процессы для сканирования, мониторинга и исправления неработающих ссылок через протокол Model Context Protocol (MCP), позволяя таким инструментам, как Claude и Cursor, напрямую управлять состоянием ссылок.
check_broken_links: сканирование URL (или карты сайта) и получение структурированного отчета о неработающих ссылках
monitor_links: настройка регулярного мониторинга веб-сайта
get_fix_suggestions: генерация рекомендаций по исправлению с учетом приоритетов
health_check: проверка работоспособности MCP-сервера и подключения к бэкенд-API
Если бэкенд-API LinkRescue недоступен, сервер переключается на реалистичные симулированные данные, чтобы локальное тестирование и демонстрации продолжали работать.
Related MCP server: webcheck-mcp
{
"mcpServers": {
"linkrescue": {
"command": "linkrescue-mcp"
}
}
}Затем спросите своего ИИ-агента:
"Просканируй example.com на наличие неработающих партнерских ссылок"
Инструмент | Бесплатно | Pro ($19/мес) | Agency ($29/мес) |
| Да | Да | Да |
| Да | Да | Да |
| - | Да | Да |
| - | - | Да |
| - | Да | Да |
| - | Да | Да |
| - | - | Да |
Оценка потери дохода | - | Да | Да |
Мониторинг нескольких сайтов | - | 5 сайтов | 25 сайтов |
Бесплатный тариф позволяет проверять неработающие ссылки на отдельных страницах. Pro открывает доступ к полноценному краулеру, предложениям по исправлению и регулярному мониторингу. Тариф Agency добавляет ежечасные проверки, вебхуки и неограниченное количество сайтов.
Перейти на Pro через MCPize — $19/мес или $190/год. Agency $29/мес или $290/год.
Установка в один клик с управляемым хостингом: Установить через 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.pyMCP-эндпоинт:
http://localhost:8000/mcp
Переменная | Описание | По умолчанию |
| Базовый URL для API LinkRescue |
|
| API-ключ для аутентифицированных запросов | пусто |
Пример:
export LINKRESCUE_API_BASE_URL="https://www.linkrescue.io/api/v1"
export LINKRESCUE_API_KEY="your-api-key"
linkrescue-mcpПолучите API-ключ на linkrescue.io/settings/api (только для тарифов Pro и Agency).
Запуск через установленную точку входа:
linkrescue-mcpЗапуск напрямую из исходного кода:
python main.pyЗапуск через FastMCP CLI:
fastmcp run main.py --transport streamable-http --port 8000Добавьте это в 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_linksВходные данные:
url (обязательно): URL сайта для сканирования
sitemap_url (опционально, тариф Agency): обход по карте сайта
max_depth (опционально, по умолчанию 3): глубина обхода
Возвращает метаданные сканирования, детали неработающих ссылок и сводную статистику. Тарифы Pro и Agency включают оценку ежемесячной потери дохода из-за неработающих партнерских ссылок.
monitor_linksВходные данные:
url (обязательно)
frequency_hours (опционально, по умолчанию 24; тариф Agency поддерживает 1)
Возвращает ID мониторинга, детали расписания и статус. Бесплатный тариф возвращает симулированный монитор (без сохранения состояния).
get_fix_suggestionsВходные данные:
полный отчет из check_broken_links, или
массив broken_links в исходном виде, или
JSON-строка в любом из этих форматов
Возвращает приоритизированные действия и предложенные шаги по исправлению. Только для тарифов Pro и Agency.
health_checkБез входных данных. Возвращает статус сервера и доступность бэкенд-API.
Этот репозиторий включает smithery.yaml и smithery.json.
Отправьте репозиторий на GitHub
Создайте/добавьте сервер в Smithery
Укажите Smithery путь к этому репозиторию
Dockerfile включен для Railway, Fly.io и других хостингов контейнеров.
# Railway
railway up
# Fly.io
fly launch
fly deployУстановите LINKRESCUE_API_BASE_URL и LINKRESCUE_API_KEY в среде вашего хоста.
Agent (Claude, Cursor, etc.)
-> MCP
LinkRescue MCP Server (this repo)
-> HTTP API
LinkRescue Backend API (linkrescue.io)Этот сервер является слоем трансляции между вызовами инструментов MCP и операциями API LinkRescue.
MIT — Создано Freedom Engineers
SelfHeal MCP — Самовосстанавливающийся прокси для MCP-серверов
SiteHealth MCP — Полный аудит состояния сайта
LeadEnrich MCP — Каскадное обогащение лидов
Версия для разработчиков: README.dev.md
Версия для маркетплейса: 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. Dates show when Glama detected each change.
v0.1.1check_broken_linksget_fix_suggestionshealth_checkmonitor_linksEach 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.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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.
Monitor MCP servers, API contracts and AI outputs for schema drift. Alerts on breaking changes.
Scan any website or MCP server for agent readiness: 0-100 score, a fix per failing check. Free.
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/carsonroell-debug/linkrescue-mcp'
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