mcp-fleet
12 MCP tools: invoice→JSON, text→SQL, PII redaction, regex & more. Free to try, pay per call.
Sollte ich dies verwenden
Qualität und Sicherheit
Befunde (3)
- LOWin json_schema_infer
- LOWin email_draft
- LOWin code_explain
Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.
Kontextkosten
Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.
Installieren
Installation mit einem Klick
Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:
{
"mcpServers": {
"mcp-fleet": {
"url": "https://mcp-fleet.nebula-labs.online/mcp/sse"
}
}
}Remote-Endpunkte
https://mcp-fleet.nebula-labs.online/mcp/ssessehttps://mcp-fleet.nebula-labs.online/mcp/sse?src=mcp_registrysseWas es kann
Tool-Inventar
Tools (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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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).
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"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.
Eingabe-Schema
{
"type": "object",
"properties": {
"code": {
"type": "string",
"description": "The function or module to generate test cases for.",
"minLength": 1,
"maxLength": 8000
}
},
"required": [
"code"
]
}Community
Nachweis