mcp-fleet
12 MCP tools: invoice→JSON, text→SQL, PII redaction, regex & more. Free to try, pay per call.
사용해야 할까요
품질 및 안전성
발견 사항 (3)
- LOWTool 'json_schema_infer' description lacks action verbjson_schema_infer에서
- LOWTool 'email_draft' description lacks action verbemail_draft에서
- LOWTool 'code_explain' description lacks action verbcode_explain에서
도구 정의와 프로토콜 준수에 대한 자동 분석을 기반으로 합니다.
컨텍스트 비용
이는 서버의 도구가 모델의 컨텍스트에 로드될 때마다 소비되는 대략적인 토큰 수입니다. 수치가 높을수록 다른 작업에 사용할 수 있는 주의가 줄어듭니다.
설치
원클릭 설치
`claude_desktop_config.json` 파일에 다음을 추가하세요:
{
"mcpServers": {
"mcp-fleet": {
"url": "https://mcp-fleet.nebula-labs.online/mcp/sse"
}
}
}원격 엔드포인트
https://mcp-fleet.nebula-labs.online/mcp/ssessehttps://mcp-fleet.nebula-labs.online/mcp/sse?src=mcp_registrysse할 수 있는 일
도구 목록
도구 (12)
⚪invoice_pdf_to_json(invoice_text)
Convert raw invoice text (extracted from PDF, email, or image OCR) into a structured JSON object. Returns vendor, date, line items, totals, and currency in a machine-readable format.
입력 스키마
{
"type": "object",
"properties": {
"invoice_text": {
"type": "string",
"description": "The raw text content of the invoice. Can be messy — include all text exactly as extracted.",
"minLength": 10
}
},
"required": [
"invoice_text"
]
}⚪text_to_compliance_checklist(requirement_text, context)
Convert a block of requirement, regulation, or specification text into a structured compliance checklist. Each checklist item includes a category, obligation, priority (HIGH/MEDIUM/LOW), and verification method.
입력 스키마
{
"type": "object",
"properties": {
"requirement_text": {
"type": "string",
"description": "The requirement or specification text to analyze. Can be a contract clause, regulatory text, internal policy, or any document describing obligations.",
"minLength": 20
},
"context": {
"type": "string",
"description": "Optional: additional context about the industry or use case (e.g. 'SaaS company, GDPR jurisdiction, healthcare data'). Helps the model assign more accurate priorities."
}
},
"required": [
"requirement_text"
]
}⚪text_to_sql(request)
Convert a natural-language data question into a SQL query. Returns the SQL, dialect, and tables used.
입력 스키마
{
"type": "object",
"properties": {
"request": {
"type": "string",
"description": "Natural-language question, optionally with a schema description.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"request"
]
}⚪json_schema_infer(sample_json)
Infer a JSON Schema (draft 2020-12) from a sample JSON document.
입력 스키마
{
"type": "object",
"properties": {
"sample_json": {
"type": "string",
"description": "A sample JSON document (object or array).",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"sample_json"
]
}⚪regex_builder(description)
Build a regular expression from a plain-language description, with an explanation and test cases.
입력 스키마
{
"type": "object",
"properties": {
"description": {
"type": "string",
"description": "What the regex should match, in plain language.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"description"
]
}🟢cron_explain(cron_expression)
Explain a cron expression in plain English and list the next run times.
입력 스키마
{
"type": "object",
"properties": {
"cron_expression": {
"type": "string",
"description": "A standard 5-field cron expression, e.g. '30 9 * * 1-5'.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"cron_expression"
]
}🟢sentiment_analyze(text)
Analyze sentiment of text with an overall score and per-aspect breakdown.
입력 스키마
{
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The text to analyze (review, message, feedback).",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"text"
]
}⚪pii_redact(text)
Locally detect and redact high-confidence PII (emails, phones, SSNs, credit cards, IPs) from text — raw data never leaves the server. Returns redacted text and found entities.
입력 스키마
{
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "Text that may contain personal data.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"text"
]
}⚪email_draft(brief)
Turn bullet points or a brief into a polished professional email (subject + body).
입력 스키마
{
"type": "object",
"properties": {
"brief": {
"type": "string",
"description": "Bullet points or a short brief describing the email to write.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"brief"
]
}⚪meeting_summarize(transcript)
Summarize a meeting transcript into a summary, action items, decisions, and participants.
입력 스키마
{
"type": "object",
"properties": {
"transcript": {
"type": "string",
"description": "The raw meeting transcript or notes.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"transcript"
]
}🟢code_explain(code)
Explain what a code snippet does, step by step, with complexity and risk notes.
입력 스키마
{
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "The source code to explain.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"code"
]
}⚪unit_test_outline(code)
Generate a unit-test outline (cases + coverage targets) for a function.
입력 스키마
{
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "The function or module to generate test cases for.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"code"
]
}커뮤니티
증거