MCP MeasureMCP Measure

Index beobachtet

Google Docs Agent by Nova (CIVAI)

I do everything related to Google Docs

Sollte ich dies verwenden

Qualität und Sicherheit

B
Qualität der Beschreibung
84%
Vollständigkeit des Schemas
93%
Qualität der Benennung
68%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Befunde (5)

  • LOWTool 'google_docs__converse' description lacks action verbin google_docs__converse
  • LOWTool 'deep_research__conduct-deep-research' doesn't follow camelCase/snake_casein deep_research__conduct-deep-research
  • LOWTool 'deep_research__conduct-deep-research' name length outside 3-30 rangein deep_research__conduct-deep-research
  • LOWTool 'deep_research__generate-research-report' doesn't follow camelCase/snake_casein deep_research__generate-research-report
  • LOWTool 'deep_research__generate-research-report' name length outside 3-30 rangein deep_research__generate-research-report

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~634Tokens (Tool-Definitionen)
~701 BTypische Antwortgröße
Minimale Auswirkung auf die Aufmerksamkeit (0.50% von 128k Kontext)

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": {
    "google-docs-agent": {
      "url": "https://nova.civai.co/mcp/agents/google-docs-agent"
    }
  }
}

Remote-Endpunkte

https://nova.civai.co/mcp/agents/google-docs-agentstreamable-http

Was es kann

Tool-Inventar

Tools (8)

🟢 Nur lesen🟡 Schreiben🔴 Löschen⚪ Unbekannt
🟡google_docs__create_doc(detail)

Run Google docs action: create doc

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "detail": {
      "type": "string",
      "description": "Task-specific details from the objective."
    }
  }
}
🟢google_docs__list_docs(detail)

Run Google docs action: list docs

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "detail": {
      "type": "string",
      "description": "Task-specific details from the objective."
    }
  }
}
🟢google_docs__get_doc(detail)

Run Google docs action: get doc

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "detail": {
      "type": "string",
      "description": "Task-specific details from the objective."
    }
  }
}
⚪google_docs__rewrite_doc(detail)

Run Google docs action: rewrite doc

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "detail": {
      "type": "string",
      "description": "Task-specific details from the objective."
    }
  }
}
🔴google_docs__delete_doc(detail)

Run Google docs action: delete doc

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "detail": {
      "type": "string",
      "description": "Task-specific details from the objective."
    }
  }
}
⚪google_docs__converse(reply_hint)

Reply conversationally when the request is ambiguous or needs clarification.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "reply_hint": {
      "type": "string",
      "description": "Optional hint for the conversational reply."
    }
  }
}
🟢deep_research__conduct-deep-research(topic, objective, breadth, depth, max_total_queries, ...)

Conduct deep, iterative research on a topic by generating multiple search queries, processing the results, and recursively exploring new research directions.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "description": "The main topic or question to research"
    },
    "objective": {
      "type": "string",
      "description": "The specific goal or objective of the research"
    },
    "breadth": {
      "type": "integer",
      "description": "Number of search queries per research direction (1-5)",
      "default": 3,
      "minimum": 1,
      "maximum": 5
    },
    "depth": {
      "type": "integer",
      "description": "Depth of recursive exploration (0-2)",
      "default": 1,
      "minimum": 0,
      "maximum": 2
    },
    "max_total_queries": {
      "type": "integer",
      "description": "Maximum number of search queries to process (2-5)",
      "default": 2,
      "minimum": 2,
      "maximum": 5
    },
    "max_duration_seconds": {
      "type": "integer",
      "description": "Maximum duration for the research process in seconds (60-300)",
      "default": 120,
      "minimum": 60,
      "maximum": 300
    },
    "connection_id": {
      "type": "string",
      "description": "Optional connection ID for the user",
      "default": ""
    }
  },
  "required": [
    "topic",
    "objective"
  ]
}
⚪deep_research__generate-research-report(research_data, objective)

Generate a comprehensive research report from existing research data.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "research_data": {
      "type": "string",
      "description": "The research data and findings to include in the report"
    },
    "objective": {
      "type": "string",
      "description": "The specific goal or objective of the report"
    }
  },
  "required": [
    "research_data",
    "objective"
  ]
}

Empfohlene Prompts

retrieve_data
Get details about [item] from Google Docs Agent by Nova (CIVAI)
Erwartete Tools: google_docs__get_doc
find_specific
Find [specific item] using Google Docs Agent by Nova (CIVAI)
Erwartete Tools: deep_research__conduct-deep-research
fetch_info
Fetch [information type] using Google Docs Agent by Nova (CIVAI)
Erwartete Tools: google_docs__get_doc
search_research
Search for information about [topic] using Google Docs Agent by Nova (CIVAI)
Erwartete Tools: deep_research__conduct-deep-research
list_items
List all [items] available in Google Docs Agent by Nova (CIVAI)
Erwartete Tools: google_docs__list_docs

Community

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Aktuelle Beobachtungen

verifiziertVersion nicht aufgezeichnet8 Tools
verifiziertVersion nicht aufgezeichnet8 Tools