Bring Sentiment Analysis
to Pydantic AI
Create your Vinkius account to connect Chattermill to Pydantic AI and start using all 11 AI tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code. No hosting, no server setup — just connect and start using.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Chattermill MCP Server?
Connect your Chattermill account to any AI agent and take full control of your customer experience (CX) intelligence through natural conversation. Unify feedback from Zendesk, App Store, Typeform, and dozens of other sources into one AI-powered view.
What you can do
- Project Management — List and inspect all feedback projects configured in your account
- Feedback Intelligence — Browse, filter, and paginate customer responses with full date and source filtering
- Theme Analysis — Explore AI-generated themes and categories to pinpoint recurring customer issues
- Metric Insights — Retrieve calculated NPS, CSAT, net sentiment, and volume metrics on demand
- Source Auditing — List all data sources and data types feeding your feedback pipeline
- Segmentation — Access custom segments for advanced cohort analysis
- Data Ingestion — Submit new feedback entries for analysis directly from your agent
How it works
- Subscribe to this server
- Enter your Chattermill API Key (found at Settings > API in your Chattermill dashboard)
- Start querying your customer insights from Claude, Cursor, or any MCP-compatible client
Who is this for?
- CX Managers — monitor sentiment trends and drill into specific customer comments using natural language
- Product Managers — identify recurring themes to prioritize features without opening the dashboard
- Insights Teams — quickly retrieve NPS and CSAT metrics for reporting straight from the chat interface
- Operations Teams — verify data source connectivity and audit feedback ingestion pipelines
Built-in capabilities (11)
Valid metric_type values: nps, average_score, net_sentiment, volume. Supports optional date range filtering with UNIX timestamps. Retrieve a calculated metric (NPS, CSAT, sentiment, volume) for a project
Use list_chattermill_projects first if the project ID is unknown. Get details of a specific Chattermill project by its ID
Returns the comment, score, metadata, and applied themes. Get detailed information for a single feedback response
Use this first to obtain the project key needed by all other Chattermill tools. The project key is typically a lowercase version of the company name. List all available feedback projects in the Chattermill account
Returns user-defined segments used for advanced filtering and cohort analysis. List custom segments defined for a project
Returns data classification types used to categorize responses. Use this to discover type keys for filtering. List all feedback data types for a project (e.g. NPS, review, survey)
Supports pagination via page/per_page and date filtering via date_from/date_to in YYYYMMDD_HHMMSS format. Default: page 1, 20 results per page, max 100. List paginated feedback responses for a specific project
Returns configured data ingestion sources. Use this to discover available source keys for filtering responses. List all feedback data sources for a project (e.g. Zendesk, App Store, Typeform)
Returns themes automatically generated by Chattermill ML to classify recurring customer topics. List AI-generated feedback themes detected in a project
Categories are parent groupings for themes, useful for high-level trend analysis. List categories that group feedback themes together
Requires the project_key plus comment text. Optionally supply score, data_source, and data_type keys from their respective list endpoints. Submit a new feedback response to a Chattermill project
Why Pydantic AI?
Pydantic AI validates every Chattermill tool response against typed schemas, catching data inconsistencies at build time. Connect 11 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.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Chattermill integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Chattermill connection logic from agent behavior for testable, maintainable code
Chattermill in Pydantic AI
Why run Chattermill with Vinkius?
The Chattermill connection runs on our fully managed, secure cloud infrastructure. We handle the hosting, maintenance, and security so you don't have to deal with servers or code. All 11 tools are ready to work instantly without any complex setup.
You stay in complete control of your data. Your AI only accesses the information you approve, keeping your sensitive passwords and private details completely safe. Plus, with automatic optimizations, your AI works faster and more efficiently.
* Every connection is hosted and maintained by Vinkius. We handle the security, updates, and infrastructure so you don't have to write code or manage servers. See our infrastructure
Over 4,000 integrations ready for AI agents
Explore a vast library of pre-built integrations, optimized and ready to deploy.
Connect securely in under 30 seconds
Generate tokens to authenticate and link external services in a single step.
Complete visibility into every agent action
Audit live requests, latency, success rates, and active security compliance policies.
Optimize spending and track token ROI
Analyze real-time token consumption and cost metrics detailed by connection.
Explore our live AI Agents Analytics dashboard to see it all working
This dashboard is included when you connect Chattermill using Vinkius. You will never be left in the dark about what your AI agents are doing with your tools.
Chattermill and 4,000+ other AI tools. No hosting, no code, ready to use.
Professionals who connect Chattermill to Pydantic AI through Vinkius don't need to write code, manage servers, or worry about security. Everything is pre-configured, secure, and runs automatically in the background.
Raw MCP | Vinkius | |
|---|---|---|
| Ready-to-use MCPs | Find and configure each manually | 4,000+ MCPs ready to use |
| Connection Setup | Manual coding & server setup | 1-click instant connection |
| Server Hosting | You host it yourself (needs 24/7 uptime) | 100% hosted & managed by Vinkius |
| Security & Privacy | Stored in plaintext config files | Bank-grade encrypted vault |
| Activity Visibility | Blind execution (no logs or tracking) | Live dashboard with real-time logs |
| Cost Control | Runaway AI token spend risk | Automatic budget limits |
| Revoking Access | Must delete files or code to stop | 1-click disconnect button |
How Vinkius secures
Chattermill for Pydantic AI
Every request between Pydantic AI and Chattermill is protected by our secure gateway. We automatically keep your sensitive data private, prevent unauthorized access, and let you disconnect instantly at any time.
Frequently asked questions
How do I find the project key I need to use with most tools?
Start by asking the agent to run list_chattermill_projects. This returns all your projects with their keys. Most Chattermill API endpoints — responses, themes, metrics, etc. — require this project key as the first parameter.
What customer experience metrics can I retrieve?
Use get_chattermill_metric with a metric type of nps, average_score, net_sentiment, or volume. You can filter by date range, category, or theme for more targeted insights. All metrics require a project key.
Where do I find my Chattermill API Key?
Log in to your Chattermill account and navigate to Settings > API. You can generate and copy your personal API Key from that section. The key is used as a Bearer token on every API request.
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 Chattermill MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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