AI Sales Agent + CRM
Evidence-first architecture for deal context, correspondence, AI output validation and controlled CRM actions.
Explore the reference →ChatGPT · GPT · MCP · API
Custom ChatGPT Plugin Development
Looking to connect ChatGPT to your website, CRM or database? These connections are often called GPT plugins. MCP (Model Context Protocol) is one way to build tool integrations for compatible AI applications.
A custom MCP integration lets ChatGPT retrieve current information from your systems and request approved actions within clearly defined permissions.
- Built around your workflow
- Uses existing APIs
- Scoped permissions
- Compatibility checked first
What does a ChatGPT plugin mean today?
The original ChatGPT Plugins provided an earlier way to extend ChatGPT. Other integration mechanisms now include GPT Actions and MCP. These are distinct technologies with different connection models and capabilities.
Original ChatGPT Plugins
An earlier ChatGPT extension mechanism based on an API description and a plugin manifest. A new project should not assume the original installation flow is available.
GPT Actions
A way to connect a custom GPT to an external API using an OpenAPI description. It suits workflows that belong inside a custom GPT, subject to feature availability.
MCP integration
Model Context Protocol is an open protocol for connecting compatible AI applications to external tools and data sources. An MCP server can call your existing APIs.
Before you commission a GPT plugin, the connection method is checked against your workflow. MCP and GPT Actions availability depends on the product, plan, mode, workspace permissions and administrator settings.
Looking for a GPT plugin developer?
Your project may need an MCP server. It connects compatible AI applications to your business systems through a standard tool interface. Explore the technology and architecture:
What can a custom ChatGPT plugin do?
ChatGPT plugin development starts with a specific workflow: which information a user needs and which actions they are allowed to request.
Work with a CRM
Find customers, review deals and retrieve interaction history from amoCRM, Bitrix24 or a custom CRM.
Query business data
Run permitted MySQL, PostgreSQL and other data queries through focused tools and restricted views.
Connect to your website
Retrieve product, order and customer information through your website API with role-based access.
Search documents
Find answers in a company knowledge base and link to sources the user is permitted to access.
Use external APIs
Retrieve current information from third-party services within their access rules and rate limits.
Automate approved actions
Create tasks, prepare documents and execute agreed operations with the required approval steps.
Capabilities depend on available APIs, permissions and tool support in the chosen AI client. Connecting a database to ChatGPT does not require unrestricted SQL access.
How a modern GPT plugin works
With MCP, a request passes through several logical components. The MCP client typically lives inside the AI application rather than being a separate user interface.
- UserDescribes the task and approves agreed changes.
- ChatGPTInterprets the request and calls an available tool when appropriate.
- MCP clientThe AI application component that maintains the connection to the MCP server.
- MCP serverExposes approved tools, validates arguments and enforces server-side restrictions.
- CRM / API / DatabaseExecutes the permitted operation and returns data within the user's access scope.
An MCP server does not give AI unrestricted access. Server-side permissions apply to every operation. Approval in the interface does not replace access checks, input validation or business rules on the server.
Practical use cases
These are example workflows for scoping an integration, not claims about completed deployments for named clients.
ChatGPT + amoCRM
“Show deals where a customer has been waiting more than two days for a reply.” Deal and message data are compared using the available APIs.
ChatGPT + an online store
“Which products are running low?” The answer uses current stock data and an agreed threshold.
ChatGPT + a company knowledge base
“Find the returns handling procedure.” Search respects document permissions.
ChatGPT + your own API
“Prepare a report on last week's orders.” Dates and metrics are validated before requesting the data.
ChatGPT + task management
“Draft tasks for overdue support requests.” Proposed tasks are shown first; creation requires approval and a server-side permission check.
Why build with MCP?
MCP can be a good fit for a custom ChatGPT connector when tools need to evolve without tying all business logic to one AI interface.
A standard tool interface
A shared approach to describing, discovering and invoking tools rather than a different format for each operation.
Reusable integration code
Server-side adapters can support several agreed workflows and clients.
Controlled data access
Permissions are enforced for the user, tool and business object on the server.
Separate responsibilities
The AI client handles the conversation; business rules and their checks stay in the integration and source systems.
Other compatible AI clients
One server can serve other applications after checking transport, authentication and tool support.
Room to grow
New tools can be added with versioning and regression checks without rebuilding every existing adapter.
MCP does not guarantee compatibility with every AI application. Protocol features, transport and authentication support are checked for each chosen client.
The development process
Technical feasibility is checked before the main build: one working scenario is verified in your intended ChatGPT environment.
Review the workflow and API
Identify users, systems, available operations and acceptance criteria.
Choose MCP or GPT Actions
Match the integration method to the interface, feature availability and environment restrictions.
Design tools and permissions
Define arguments, outputs, roles, read access, write access and approval steps.
Build the integration
Implement API adapters, error handling, timeouts and safe retries.
Configure authentication and security
Set up a suitable sign-in mechanism, server-side restrictions and secret storage.
Test with ChatGPT
Check real workflows, connection behaviour, API errors and permission boundaries.
Deploy and document
Prepare hosting, HTTPS, connection instructions, monitoring and an update procedure.
Security for business integrations
The design accounts for access boundaries, data sensitivity and the consequences of each action. A protocol alone does not secure an application.
Authentication
OAuth or another suitable mechanism is selected for the client and infrastructure. Tokens, roles and object-level permissions are verified.
Least privilege
Focused tools and read-only access are the starting point. Writes are enabled only for agreed workflows.
Request limits
Rate, size and execution-time limits work alongside argument validation and retry controls.
Isolation and API keys
Internal services sit behind a restricted integration layer. Secrets stay on the server and out of prompts and tool responses.
Audit trails
Logs capture the initiator, operation and outcome with sensitive fields masked and log access restricted.
Approval before changes
Proposed changes are shown for approval, followed by fresh server-side permission checks. Documents and API responses are treated as untrusted data.
Relevant engineering experience
Integration and automation design draws on long-standing experience with PHP, Laravel, APIs, CRM and AI systems. The materials below also appear on the MCP service page: they are real architectural examples and public projects, not invented testimonials or claimed deployment results.
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AI-native scheduling and CRM architecture with knowledge retrieval and workflow automation.
Explore the reference →RAG Content Pipeline
Public architecture and code examples: LLM classification, Qdrant, quality checks and controlled publishing.
Explore the reference →IdeaRadar
A public product for discovering and evaluating software projects, showing data processing and product engineering.
Explore the reference →How much does ChatGPT plugin development cost?
The estimate depends on the number of systems, API complexity and readiness, tool count, authentication requirements and testing scope.
One workflow, one system
For example, finding CRM deals or retrieving a report from your own API.
Multiple systems and roles
Connected operations, several data sources, OAuth and access boundaries.
Actions that change data
Approval flows, protection against duplicate writes, audit logs and additional checks.
A fixed price is not assigned before discovery. Hosting and third-party API usage are estimated separately.
ChatGPT plugin development: questions and answers
Can a custom ChatGPT plugin still be built?
A custom connection to your systems can be developed. Depending on the task, this may use MCP or GPT Actions. It does not mean recreating the original ChatGPT Plugins installation model; the selected connection method is checked first.
How is MCP different from the original ChatGPT Plugins?
The original ChatGPT Plugins were a specific extension mechanism for ChatGPT. MCP is an open protocol for connecting compatible AI applications to external tools and data. Their architecture, setup and supported clients differ.
Should the project use GPT Actions or MCP?
GPT Actions connects a custom GPT to an API described with OpenAPI. MCP connects tools to a compatible AI client and can support reuse of the server integration. The right choice depends on the interface, actions, authentication and available features.
Can ChatGPT connect to a custom website?
Yes, when the website exposes a suitable API or can support a restricted server-side interface. Product, order and other approved information can be retrieved without giving ChatGPT access to the admin panel.
Can ChatGPT connect to a CRM?
Yes, when the required APIs and permissions are available. A CRM integration can support customer and deal search, interaction history and task preparation. Operations are verified for your CRM and ChatGPT environment.
Can ChatGPT change data through MCP?
It can request write tools when the client supports them and the server permits the operation. The project includes approval before changes, permission checks, validation and duplicate-operation protection. Tool access alone is not permission to write.
Does a plugin need to be installed in ChatGPT?
Setup depends on the mechanism. MCP uses an app or connector connection in a supported ChatGPT environment; GPT Actions is configured in a custom GPT. Availability depends on the plan, mode, permissions and workspace settings.
Can one MCP server work with different AI applications?
Yes, with compatible clients after transport, authentication and tool support have been checked. Compatibility with every AI application is not assumed.
How much does a GPT plugin cost?
The estimate follows a review of system and tool count, API quality, authentication, hosting and testing. Scope and pricing are prepared after discovery rather than presented as an unsupported fixed fee.
How long does development take?
The schedule is defined after API and connection checks. A focused read-only workflow and a multi-system integration with write actions require different levels of work. Milestones are agreed after the initial assessment.
Can the MCP server run on a private VPS?
Yes, including in Docker. A remote ChatGPT connection needs a supported HTTPS transport and a secure endpoint reachable by that client. Private infrastructure needs an agreed network access method; a local stdio process on a VPS alone is insufficient for a cloud client.
Related case studies and reading
MCP use cases for a websiteExamples of AI working with website tools and data.
MCP and RAG for working with business dataConnecting data retrieval with tools and result presentation.
FIGHTEX: economy and tokenomics simulatorAn example of game economy modelling and scenario testing.
MCP / AI INTEGRATION
Need a custom ChatGPT plugin?
Describe the workflow and the systems you want to connect. A suitable integration architecture and a development estimate will be prepared for your project.
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