How do I get a DocsBot API Key?

Log in to your DocsBot account, navigate to the API section in your account settings, and you can generate or retrieve your unique API Key from there.

Can the agent train my bots?

This integration currently focuses on querying bots and auditing sources. Adding new sources or triggering full training cycles should be managed via the DocsBot dashboard.

Which AI models are used?

DocsBot supports various models including GPT-3.5 and GPT-4. The agent will retrieve results based on the specific model configured for your bot in your account.

DocsBot MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: DocsBot
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect DocsBot through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to DocsBot "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in DocsBot?"
    )
    print(result.data)

asyncio.run(main())

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About DocsBot MCP Server

Integrate DocsBot, the AI-powered knowledge base platform, directly into your AI workflow. Manage your custom AI bots, track their data sources (URLs, PDFs, documents), monitor indexing status, and query your bots directly using natural language.

Pydantic AI validates every DocsBot tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Bot Oversight — List and retrieve detailed configuration and metadata for all the AI bots in your team.
Knowledge Management — Monitor data sources used to train your bots and track their last indexing timestamps.
Bot Interaction — Query your bots directly via the agent to retrieve AI-generated answers based on your knowledge base.
Analytics & Logs — Access technical logs of recent bot interactions, including questions and generated answers.

The DocsBot MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect DocsBot to Pydantic AI via MCP

Follow these steps to integrate the DocsBot MCP Server with Pydantic AI.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 10 tools from DocsBot with type-safe schemas

Why Use Pydantic AI with the DocsBot MCP Server

Pydantic AI provides unique advantages when paired with DocsBot through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your DocsBot integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your DocsBot connection logic from agent behavior for testable, maintainable code

DocsBot + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the DocsBot MCP Server delivers measurable value.

Type-safe data pipelines: query DocsBot with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple DocsBot tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query DocsBot and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock DocsBot responses and write comprehensive agent tests

DocsBot MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect DocsBot to Pydantic AI via MCP:

ask_bot_question

Ask a technical question to a specific DocsBot and retrieve an AI-generated answer

get_bot_details

Get detailed settings and information for a specific bot

get_bot_knowledge_summary

Retrieve a high-level summary of the knowledge base size and source count

get_docsbot_account_metadata

Retrieve metadata for the current authenticated user

list_bot_interaction_logs

List recent questions and answers handled by a specific bot

list_bot_knowledge_sources

List all data sources (URL, PDF, etc.) used to train a specific bot

list_docsbot_teams

List all teams you are a member of in DocsBot

list_recently_indexed_bots

Identify bots that have had their knowledge base updated recently (mock logic)

list_team_bots

List all AI bots configured within a specific team

search_bot_sources

Search for specific knowledge sources by name keyword

Example Prompts for DocsBot in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with DocsBot immediately.

"Ask our 'API Docs Bot': 'How do I authenticate using the SDK?'."

"List all data sources used by our 'Support Bot'."

"Show me the last 5 questions asked to the 'Sales Bot'."

Troubleshooting DocsBot MCP Server with Pydantic AI

Common issues when connecting DocsBot to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

DocsBot + Pydantic AI FAQ

Common questions about integrating DocsBot MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your DocsBot MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.