MCP Server Development · Dallas, TX

We Build Custom MCP Servers

The Model Context Protocol (MCP) is an open standard developed by Anthropic that defines how AI applications connect to external data sources and tools. An MCP server exposes a set of typed, authenticated endpoints that AI clients, including Claude, Cursor, and other MCP-compatible tools, can call during a reasoning session. Instead of copy-pasting data into a prompt or building one-off integrations for every AI tool you want to connect, you build one MCP server and any compatible AI client can use it. It is the API layer for AI-native applications, and it is becoming the standard way serious teams connect AI to business infrastructure.

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Typical turnaround 2-4 weeks · No long-term contract

Example MCP Tool Definition

{
  "name": "get_client_record",
  "description": "Fetch a client record from Twenty CRM by company name",
  "inputSchema": {
    "type": "object",
    "properties": {
      "company_name": { "type": "string" }
    },
    "required": ["company_name"]
  }
}

The Case

Why Your Business Needs a Custom MCP Server

Your AI tools are only as useful as the data they can access. If your team is using Claude, Cursor, or a custom AI agent for anything beyond writing, they are hitting a ceiling: the AI can only work with information you paste into the prompt manually.

A custom MCP server eliminates that ceiling. You wrap your internal API, your CRM, your billing system, or your proprietary database in an MCP server, and now any compatible AI tool can query it directly during a task. The AI does not need a human to pull the data. It calls the tool, gets the result, and continues.

The practical implication: an AI agent that can read your CRM can draft a proposal with actual deal data in it. An agent that can call your billing API can check whether an invoice was paid before triggering the onboarding sequence. An agent with access to your internal knowledge base can answer support tickets without escalation. None of that is possible with a standard prompt. All of it is possible once you have an MCP server.

For SaaS companies, there is an additional angle: if your customers are using Claude or Cursor, giving them an MCP server for your product turns your platform into an AI-native tool. Your competitors who do not offer that will look like legacy software by comparison.

Photo: Pexels

Portfolio

MCP Servers We Have Shipped

These are production integrations built for the Whtnxt agent stack. We use every one of them in daily operations.

Business Operations

Twenty CRM — contact lookups, deal reads, task creation, timeline logging
InvoiceNinja — invoice management, payment status checks, product catalog access, client records
Documenso — contract generation, template selection, signing status monitoring
Listmonk — subscriber management, campaign creation, list segmentation

Content and Productivity

Google Workspace — Gmail, Google Calendar, Google Meet scheduling (OAuth2 with custom scopes)
GA4 Admin — analytics key event writes and property management
Google Search Console — performance queries, index status, coverage data
Pexels — image search and retrieval for content publishing workflows
Formbricks — form submission reads and survey management

Infrastructure and Internal Systems

NocoDB — multi-base reads and writes across operational data tables
Outline — document creation, knowledge base search, SOP retrieval
Synology DS NAS — file management and Docker container management via the NAS API
Camofox browser automation — headless browser session management for web-based scraping and form interactions

Each of these went from zero to production in two to four weeks. Most involved OAuth2 implementation, typed tool definitions, error handling, and Stdio transport setup. None use off-the-shelf MCP wrappers.

Use Cases

Four Scenarios Where a Custom MCP Server Changes What Is Possible

Connect Your Proprietary Data to AI Tools

If your business has internal systems, databases, or APIs that are not publicly available, your AI tools cannot access them without a custom integration. An MCP server gives Claude, Cursor, or your own agent stack structured read and write access to that data without exposing credentials or rebuilding your security architecture.

Give Your Team AI-Native Access to Your Core Business Tools

CRM, billing, contracts, email: your team uses these every day, and your AI tools cannot touch any of them without copy-paste or manual exports. A custom MCP server for each system means your agents can look up a client record, check payment status, generate a contract, and send a follow-up email in a single orchestrated workflow. No manual handoffs.

Make Your SaaS Product AI-Native

If you run a SaaS platform and your customers are adopting Claude, Cursor, or other AI tools, an MCP server for your product turns you into a first-class AI integration. Your customers can query your product's data, trigger workflows, and build AI automations on top of your platform without leaving their AI environment. This is a retention and acquisition lever that most SaaS companies have not figured out yet.

Custom Integrations That Do Not Fit Off-the-Shelf Options

Existing MCP libraries cover the major consumer tools (GitHub, Slack, Google Drive). They do not cover your industry-specific ERP, your in-house data warehouse, or the legacy CRM your enterprise client still runs. We scope and build integrations for systems that do not have community MCP servers and are unlikely to get them.

Our Process

From Scope to Shipped in Four Steps

Scope

We review your API documentation, auth architecture, and the specific tool-use cases your agents need to cover. We identify which endpoints matter, what data shapes to expect, and where authentication will require custom handling. Output: a written spec with typed tool definitions and estimated build time.

Design

We define the MCP tool schema: names, descriptions, input parameters, return types. We write them to match how an LLM will actually call them during a reasoning session, not just how an engineer would design a REST API. The tool descriptions are part of the prompt context the AI sees. They matter more than most API documentation.

Build

We build the MCP server using the TypeScript or Python SDK, implement authentication (API key, OAuth2, or custom session handling), add error handling and retry logic, and write tests against the live API. We use Stdio transport for Claude Code and local agent use cases, with HTTP transport available for hosted deployments.

Ship and Support

We deliver the server code, documentation, and deployment instructions. For teams hosting on their own infrastructure, we provide the Docker setup. Optional maintenance retainer covers API version updates, auth token rotation, and new tool additions as your needs evolve.

Pricing

What an MCP Server Costs to Build

A typical custom MCP server build runs $3,000 to $8,000 depending on the number of tools exposed, the complexity of the authentication flow, and whether the target API has clean documentation. APIs with OAuth2 and inconsistent documentation sit at the higher end. Well-documented REST APIs with API key auth are usually toward the lower end.

Optional hosting and maintenance retainer starts at $500/month and covers API version compatibility, auth token management, and adding new tool definitions as your integrations expand.

FAQ

MCP Development FAQ

What is the Model Context Protocol?

MCP is an open standard developed by Anthropic that defines how AI clients like Claude communicate with external tools and data sources. An MCP server exposes typed, callable functions that the AI can invoke during a task, the same way a function in code can call an external API. It separates the AI layer from the tool layer, which means you build one server and any MCP-compatible AI client can use it. The spec is public and adoption is accelerating across the AI tooling ecosystem.

When should we build a custom MCP server instead of using an existing one?

Use an existing community MCP server if one exists for your tool and covers your use case. Build a custom one when: your tool does not have a community server, the existing server does not expose the endpoints you need, your system requires custom authentication that a generic wrapper does not handle, or you need the tool definitions written specifically for how your agents will use them in context. For business-critical integrations, custom servers also give you control over versioning and reliability.

What does an MCP server cost to build?

Typical builds run $3,000 to $8,000. The main variables are the number of tool definitions, the complexity of the auth implementation, and how well-documented the target API is. An optional maintenance retainer starts at $500/month for ongoing support, API updates, and new tool additions.

Do you provide ongoing support and maintenance?

Yes. APIs change, auth tokens expire, and your tool-use requirements will grow as your agent stack matures. The optional maintenance retainer covers API version updates, token rotation, and adding new tool definitions. For clients running multi-agent systems, we typically recommend the retainer because a broken MCP server in a production agent pipeline is a blocking issue, not a nice-to-fix.

Get an MCP Development Quote

Tell us what system you need to connect and what your agents need to do with it. We will scope it, give you a fixed-price estimate, and start in the next available sprint.

Get an MCP Development Quote

Fixed-price builds · Typical turnaround 2-4 weeks · No long-term contract