깨진 제휴 링크를 빠르게 찾고, 영향도에 따라 우선순위를 지정하며, AI 에이전트가 수행할 수 있는 수정 제안을 생성하세요.
단 한 번의 호출로 38개 이상의 제휴 네트워크를 확인하고 예상 수익 손실을 계산합니다.
원클릭 설치: MCPize에서 설치 |
pip install linkrescue-mcp
LinkRescue MCP는 모델 컨텍스트 프로토콜(MCP)을 통해 깨진 링크 스캔, 모니터링 및 수정 워크플로우를 제공하므로 Claude나 Cursor와 같은 도구에서 직접 링크 상태 작업을 실행할 수 있습니다.
check_broken_links: URL(또는 사이트맵)을 스캔하고 구조화된 깨진 링크 보고서를 반환합니다.
monitor_links: 웹사이트에 대한 주기적인 모니터링을 설정합니다.
get_fix_suggestions: 우선순위가 지정된 수정 권장 사항을 생성합니다.
health_check: MCP 서버 및 백엔드 API 연결을 확인합니다.
LinkRescue 백엔드 API에 연결할 수 없는 경우, 서버는 로컬 테스트 및 데모가 계속 작동하도록 현실적인 시뮬레이션 데이터로 대체합니다.
Related MCP server: webcheck-mcp
{
"mcpServers": {
"linkrescue": {
"command": "linkrescue-mcp"
}
}
}그런 다음 AI 에이전트에게 다음과 같이 요청하세요:
"example.com에서 깨진 제휴 링크를 스캔해 줘"
도구 | 무료 | 프로 ($19/월) | 에이전시 ($29/월) |
| 예 | 예 | 예 |
| 예 | 예 | 예 |
| - | 예 | 예 |
| - | - | 예 |
| - | 예 | 예 |
| - | 예 | 예 |
| - | - | 예 |
예상 수익 손실 | - | 예 | 예 |
다중 사이트 모니터링 | - | 5개 사이트 | 25개 사이트 |
무료 티어는 단일 페이지 깨진 링크 확인을 제공합니다. 프로 티어는 전체 크롤러 + 수정 제안 + 주기적 모니터링 기능을 잠금 해제합니다. 에이전시 티어는 시간별 확인, 웹훅 및 무제한 사이트 수를 추가합니다.
MCPize에서 프로로 업그레이드 — 월 $19 또는 연 $190. 에이전시 티어는 월 $29 또는 연 $290.
관리형 호스팅을 통한 원클릭 설치: MCPize에서 설치
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 엔드포인트:
http://localhost:8000/mcp
변수 | 설명 | 기본값 |
| LinkRescue API 기본 URL |
|
| 인증된 요청을 위한 API 키 | 비어 있음 |
예시:
export LINKRESCUE_API_BASE_URL="https://www.linkrescue.io/api/v1"
export LINKRESCUE_API_KEY="your-api-key"
linkrescue-mcplinkrescue.io/settings/api에서 API 키를 받으세요 (프로 및 에이전시 티어 전용).
설치된 진입점을 통해 실행:
linkrescue-mcp소스에서 직접 실행:
python main.pyFastMCP CLI를 통해 실행:
fastmcp run main.py --transport streamable-http --port 8000claude_desktop_config.json에 다음을 추가하세요:
{
"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_links입력:
url (필수): 스캔할 사이트 URL
sitemap_url (선택 사항, 에이전시 티어): 사이트맵에서 크롤링
max_depth (선택 사항, 기본값 3): 크롤링 깊이
스캔 메타데이터, 깨진 링크 세부 정보 및 요약 통계를 반환합니다. 프로 및 에이전시 티어에는 깨진 제휴 링크에 대한 예상 월간 수익 손실이 포함됩니다.
monitor_links입력:
url (필수)
frequency_hours (선택 사항, 기본값 24; 에이전시 티어는 1 지원)
모니터링 ID, 일정 세부 정보 및 상태를 반환합니다. 무료 티어는 시뮬레이션된 모니터를 반환합니다(지속성 없음).
get_fix_suggestions입력:
check_broken_links의 전체 보고서, 또는
원시 broken_links 배열, 또는
두 형식 중 하나의 JSON 문자열
우선순위가 지정된 작업과 제안된 수정 단계를 반환합니다. 프로 및 에이전시 티어 전용입니다.
health_check입력 없음. 서버 상태 및 백엔드 API 연결 가능 여부를 반환합니다.
이 저장소에는 smithery.yaml 및 smithery.json이 포함되어 있습니다.
저장소를 GitHub에 푸시
Smithery에서 서버 생성/추가
Smithery가 이 저장소를 가리키도록 설정
Railway, Fly.io 및 기타 컨테이너 호스트를 위한 Dockerfile이 포함되어 있습니다.
# Railway
railway up
# Fly.io
fly launch
fly deploy호스트 환경에서 LINKRESCUE_API_BASE_URL 및 LINKRESCUE_API_KEY를 설정하세요.
Agent (Claude, Cursor, etc.)
-> MCP
LinkRescue MCP Server (this repo)
-> HTTP API
LinkRescue Backend API (linkrescue.io)이 서버는 MCP 도구 호출과 LinkRescue API 작업 간의 변환 계층입니다.
MIT — Freedom Engineers 제작
SelfHeal MCP — MCP 서버용 자가 치유 프록시
SiteHealth MCP — 전체 웹사이트 상태 감사
LeadEnrich MCP — 워터폴 리드 강화
개발자 중심 버전: README.dev.md
마켓플레이스 중심 버전: 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.
