Byteplant’s MCP server connects AI assistants and workflows to live postal address, email and phone validation via the Model Context Protocol (MCP).
Designed for developers and automated ecommerce platforms, it catches bad data before it leads to failed shipments, bounced emails, or wasted calls.
Explore our tools, integration guides, and learn how MCP keeps your customer data clean and reliable.
Check out Byteplant's MCP Github repository with additional installation instructions.
Check out Byteplant's MCP server PyPi package.
Download the official .mcpb server.
Turn your AI agents and workflows into powerful data-validation assistants. Byteplant’s MCP server bridges your LLM directly to global validation engines covering 240+ countries and territories.
No custom API code required.
Address-Validator verifies streets, house numbers, postal codes, and cities against global reference datasets to fix delivery errors upstream.
Email-Validator checks syntax, domains, mail servers and mailbox availability. Disposable and Catch-all email addresses are flagged automatically.
Phone-Validator checks phone number validity and identifies line types, including mobile, fixed-line and VoIP. National and international formats help applications prepare consistent records for customer service and outbound communication.
Automatically reformat non-standard address, email, and phone number entries into normalized fields, simplifying database maintenance and data hygiene.
Address-Geocoding appends rooftop accurate geocoordinates during address validation. Use the returned coordinates in territory planning, mapping or to plan logistics processes.
Provide your LLMs with granular validation reports so assistants can suggest intelligent corrections to your workflows in real time.
An address can look perfectly valid while containing the wrong postal code, a correctly formatted email address can easily belong to a closed mailbox, and a phone number that passes a simple regex check can still be invalid. Static training data simply cannot tell an AI model whether a customer’s contact details are active today. Connecting AI systems to Byteplant’s MCP server gives them instant access to live checks whenever a workflow demands them.
When an AI assistant handles a new customer record, it queries Byteplant, analyzes the structured response, and explains to the user or system exactly which field needs attention. Teams spend far less time manually investigating records that only passed a superficial format check. This expands AI capabilities where accuracy directly impacts operational performance—from capturing clean lead data and reducing address-related delivery failures to maintaining pristine customer databases. The model interprets the request, while Byteplant performs the real-time contact validation.
Byteplant delivers a ready-to-use validation tool via its MCP servers, eliminating the need for custom API integration code. Your AI assistant can seamlessly combine Byteplant with authorized connections to popular enterprise systems, such as CRMs or order management platforms. Each tool handles its specific task, while the host application orchestrates the entire workflow. This approach empowers both conversational AI interfaces and autonomous agents across development environments, letting your team stay in its preferred platform while adding real-time contact validation to its core capabilities.
Integrating live validation into autonomous workflows enables AI agents to handle end-to-end data processing without compromising accuracy. For example, during vendor onboarding, an AI agent can extract submitted contact details, query Byteplant via MCP to validate the address, verify the email mailbox, and parse the phone number—all before adding the data to your database.
Because Byteplant returns granular JSON validation details, your AI model can compare original entries against standardized corrections, log precise audit metadata, and use your CRM’s native tool to create the updated record. The host LLM acts as the central orchestrator, executing validation rules that determine whether a record can proceed automatically or requires human intervention. This gives engineering teams full control over automated data pipelines across onboarding, checkout, account updates, and more.
Byteplant delivers enterprise-grade location services grounded in strict European data protection standards. As an European company, we enforce Privacy-by-Design principles and host our infrastructure exclusively within the EU.
Customer records are processed solely for specified validation workflows under stringent access controls. For full GDPR compliance, we enforce a strict 14-day retention policy: all validation and enrichment-related data is automatically and permanently deleted from our servers after 14 days. Byteplant provides the ideal compliance framework for risk-averse enterprises handling sensitive customer data.
Connect Byteplant to an MCP host that supports locally running servers over stdio.
Start a free trial of the Email-Validator, Phone-Validator or Address-Validator and copy the API key from your account.
Each tool uses its own key:
EV_TOKEN for emailPV_TOKEN for phoneAV_TOKEN for addressesYou only need the keys for the services you use.
Install uv, then add a byteplant server that runs uvx byteplant-mcp@latest with your keys as environment variables:
mcpServers and restart. Or install the MCPB extension under Settings → Extensions → Advanced settings.claude mcp add with an --env option per key.~/.cursor/mcp.json.vscode/mcp.json
Restart your host and check that validate_email, validate_phone and validate_address appear in its tool list. Then ask, for example:
Byteplant’s MCP server is free to use for all Address-, Email-, and Phone-Validators.
Create your Byteplant account to get started, or contact our team to discuss your integration and validation requirements.
No credit card required
Byteplant’s MCP server connects MCP-compatible AI applications to our postal address, email and phone validation services. Your LLM can request checks and use the returned results within a broader business process.
The server translates supported tool requests into calls to our validation APIs and presents the returned API response to your LLM, which then interprets the result in your workflow.
Byteplant’s MCP server supports Address-Validator, Email-Validator, and Phone-Validator APIs.
You only need to configure the settings between your LLM and Byteplant’s MCP server. Our MCP server will then handle all of the complex backend for validating and returning the validation results to you.
The following technical overview covers the components behind the connection. MCP uses a client-server architecture: the MCP host manages the AI application, an MCP client communicates with a server, and that server supplies specific capabilities. A host can manage several clients connected to different servers.
This standardized protocol takes inspiration from the Language Server Protocol, which separates support for programming languages from individual editors. For AI tool integration, a shared interface similarly reduces repeated integration work across applications.
MCP servers expose capabilities through tools, resources and prompts. Tools perform tasks, such as querying an external service. Resources provide context from data sources, including files, databases and content repositories. Prompts supply reusable templates for defined interactions. Individual servers implement the features relevant to their purpose.
A server might connect an assistant to search engines or code repositories; Byteplant’s server exposes validation tools. Together, suitable connections help AI systems access real-time data and relevant information from external data sources when a task requires them.
Distinguish a failed tool invocation from an invalid contact record. A timeout, depleted account or rate limit requires different handling from a rejected address. Preserve the returned status, apply bounded retries where appropriate and show unresolved checks in the workflow. This prevents temporary service conditions from being recorded as customer data errors.
MCP versus RAG describes two different parts of an AI application. Retrieval-augmented generation focuses on retrieving relevant information from documents or other knowledge sources to support a response. MCP specifies how applications connect to tools and context providers. A retrieval service can itself be available through MCP.
For example, an assistant might retrieve your shipping policy, then validate a customer’s address with Byteplant. The retrieved policy explains service conditions; the validation result informs how the submitted address is handled. Both support the same customer interaction.
When comparing available MCP servers, check the publisher, supported transport, authentication requirements, permissions and returned information. An MCP server may run on the same machine as its host or on a remote system. A locally running server can access both local resources and remote resources, including online APIs. These choices affect which MCP server implementations your application can use.
An AI-powered IDE or a coding tool such as Claude Code provides one setting for MCP; customer-facing LLM applications provide another. The open interface reduces integration-related vendor lock-in and enables developers to reuse connections across compatible hosts.
Review permissions as connected systems expand. Restricting executable packages, protecting credentials and limiting accessible data helps address security threats in tool-enabled applications.