Beschreiben Sie einen beliebigen Geschäftsprozess. Erhalten Sie einen visuellen KI-Automatisierungs-Workflow, den Sie bearbeiten, exportieren und teilen können – innerhalb von Claude, Cursor, Windsurf, Ihrem Terminal oder einem beliebigen MCP-kompatiblen Client.
Builder kostenlos ausprobieren | Website
Dieser MCP-Server verbindet Ihren KI-Assistenten oder Coding-Agenten mit dem Obscura AI Workflow Builder. Wenn Sie ihn bitten, eine Geschäftsautomatisierung abzubilden, generiert er einen interaktiven visuellen Workflow und gibt Ihnen einen Link zum Öffnen, Bearbeiten und Teilen.
Kein API-Schlüssel. Keine Registrierung. Keine Kosten.
Sie: „Erstelle eine Automatisierung für den Gäste-Check-in in meinem Hotel“
Claude / Cursor / Ihr Agent: Hier ist Ihr Workflow – 6 Schritte, die von der Buchungsbestätigung bis zur Bewertung nach dem Aufenthalt alles abdecken.
Workflow öffnen: https://obscuraai.xyz/builder?d=eJxVjk...
Der Link öffnet eine visuelle Arbeitsfläche, auf der Sie Knoten verschieben, Beschriftungen bearbeiten sowie den Ablauf exportieren oder teilen können.
Related MCP server: task-orchestrator
Client | Typ |
Claude Desktop | Desktop-App |
Claude Code | CLI / Terminal |
Cursor | IDE |
Windsurf | IDE |
Cline | VS Code-Erweiterung |
Continue.dev | VS Code / JetBrains |
Zed | Editor |
Jeder MCP-stdio-Client | CLI / benutzerdefinierte Agents |
HTTP direct | LangChain, CrewAI, benutzerdefinierte LLMs |
Branche | Beispiel-Workflows |
Gastgewerbe | Gästekommunikation, Tischreservierungen, Lieferantenkoordination, Veranstaltungsplanung |
Immobilien | Immobilien-Pipeline, Mieterverwaltung, Dokumentenverarbeitung |
Agenturen | Kunden-Onboarding, Content-Pipeline, Lead-Qualifizierung |
Recht | Dokumentenintelligenz, Fallaufnahme, Compliance |
Finanzen | KYC-Prüfungen, Reporting-Pipelines, Rechnungsautomatisierung |
14 vorgefertigte Workflow-Vorlagen mit branchenspezifischer Anpassung der Beschriftungen.
Fügen Sie dies Ihrer Konfigurationsdatei hinzu:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"obscura": {
"command": "npx",
"args": ["-y", "@obscuraai/mcp-server"]
}
}
}Starten Sie Claude Desktop neu. Sie werden "obscura" in Ihren verbundenen Tools sehen.
claude mcp add obscura -- npx -y @obscuraai/mcp-serverFügen Sie dies Ihren MCP-Einstellungen hinzu (normalerweise mcp.json oder ähnlich):
{
"mcpServers": {
"obscura": {
"command": "npx",
"args": ["-y", "@obscuraai/mcp-server"]
}
}
}npx -y @obscuraai/mcp-serverDer Server spricht JSON-RPC über stdin/stdout – leiten Sie Nachrichten direkt von jedem Agenten weiter.
POST https://obscuraai.xyz/api/mcp
Content-Type: application/jsonKeine Authentifizierung erforderlich. Rufen Sie ihn von jedem HTTP-fähigen Agenten oder LLM-Framework aus auf.
generate_obscura_workflowGeneriert einen visuellen Workflow und gibt einen teilbaren Link zur interaktiven Arbeitsfläche zurück.
Parameter | Typ | Erforderlich | Beschreibung |
| string | Ja | Der zu automatisierende Geschäftsprozess |
| string | Nein |
|
Versuchen Sie, Ihren Agenten zu fragen:
„Erstelle eine Automatisierung für die Lead-Qualifizierung in meiner Agentur“
„Entwirf einen Gästekommunikations-Workflow für ein Hotel in Dubai“
„Baue eine Pipeline zur Dokumentenverarbeitung für eine Anwaltskanzlei“
„Erstelle einen Ablauf für Mieter-Wartungsanfragen für die Immobilienverwaltung“
Obscura AI entwickelt maßgeschneiderte KI-Workflows, Agents und Automatisierungen für moderne Unternehmen. Wir implementieren Systeme in Tagen, nicht in Monaten.
Gründer: Prince Adza
Website: obscuraai.xyz
E-Mail: prince@obscuraai.xyz
WhatsApp: +447466314822
Bediente Märkte: Dubai, Monte Carlo, Südafrika, Singapur, London und expandierend in Nordamerika und Europa.
MIT
Generate a visual AI automation workflow for a business process. Returns a shareable link to an interactive canvas on obscuraai.xyz where the workflow can be viewed, edited, and exported. Use when a user wants to map out an automation, workflow, or AI system for their business.
| Name | Required | Description | Default |
|---|---|---|---|
| description | Yes | Describe the business process to automate. Include the industry and specific pain point if possible. | |
| industry | No | Primary industry (optional) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output ('shareable link') and that the workflow can be 'viewed, edited, and exported,' but lacks details on permissions, rate limits, error handling, or whether the generation is idempotent. For a tool with no annotations, this leaves significant gaps in understanding its behavior.
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 concise and front-loaded, stating the purpose and output in the first sentence. The second sentence adds usage context without redundancy. Both sentences earn their place, but minor improvements in clarity could push it to a perfect score.
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 no annotations and no output schema, the description partially compensates by explaining the output format and usage. However, it lacks details on behavioral traits like error conditions or performance, and does not fully address the complexity of a workflow generation tool. It is minimally adequate but has clear gaps.
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 description does not explicitly discuss parameters, but the input schema has 100% description coverage, clearly documenting both parameters. The description implies parameter use through 'Describe the business process to automate' but adds no additional meaning beyond the schema. With high schema coverage, the baseline score of 3 is appropriate.
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: 'Generate a visual AI automation workflow for a business process.' It specifies the verb ('Generate') and resource ('visual AI automation workflow'), and mentions the output ('Returns a shareable link to an interactive canvas on obscuraai.xyz'). However, since there are no sibling tools, it cannot distinguish from alternatives, preventing a perfect score.
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 some usage guidance: 'Use when a user wants to map out an automation, workflow, or AI system for their business.' This implies the context but does not explicitly state when not to use it or compare to alternatives. Since there are no sibling tools, the lack of alternatives is understandable, but the guidance remains basic.
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.
v1.0.4generate_obscura_workflowScored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.
Since there is only one tool, naming consistency is inherently perfect. The tool name 'generate_obscura_workflow' follows a clear verb_noun pattern, but there are no other tools to assess consistency across a set.
A single tool is too few for the server's apparent purpose of generating and managing visual AI automation workflows, as it lacks operations for viewing, editing, or exporting workflows beyond the initial generation. This minimal scope will likely cause agent failures due to incomplete functionality.
The tool surface is severely incomplete for the domain of workflow automation; it only provides generation without any CRUD operations (e.g., no tools to list, update, delete, or retrieve existing workflows), leaving significant gaps that will hinder agent effectiveness.
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