MCP MeasureMCP Measure

index observed

Google Docs Agent by Nova (CIVAI)

I do everything related to Google Docs

Should I use this

Quality & Safety

B
Description quality
84%
Schema completeness
93%
Naming quality
68%
Poisoning risk
100%
Permission match
100%
Protocol compliance
100%

Findings (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

Based on automated analysis of tool definitions and protocol compliance.

Context Cost

~634Tokens (tool definitions)
~701 BTypical response size
Minimal attention impact (0.50% of 128k context)

This is the approximate number of tokens consumed each time the server's tools are loaded into a model's context. Higher counts reduce the attention available for other tasks.

Install

One-Click Install

Add this to your `claude_desktop_config.json` file:

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

Remote endpoints

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

What it can do

Tool inventory

Tools (8)

🟢 Read-only🟡 Write🔴 Delete⚪ Unknown
🟡google_docs__create_doc(detail)

Run Google docs action: create doc

Input 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

Input 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

Input 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

Input 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

Input 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.

Input 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.

Input 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.

Input 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"
  ]
}

Recommended Prompts

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

Community

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Evidence

Recent observations

verifiedversion not recorded8 tools
verifiedversion not recorded8 tools