Chat with your company information.
A custom MCP server lets your team ask questions in natural language and get answers from your real systems: databases, APIs, SaaS tools, documents, and internal workflows.
MCP is how AI talks to your company systems.
In plain language: MCP lets you chat with the information inside your company. Instead of opening dashboards, exporting CSVs, writing SQL, or asking someone from operations to check a system, you can ask an AI assistant a question in natural language.
The MCP server sits between the AI and your business systems. When someone asks a question, the AI does not receive unlimited access to everything. It can only use the tools that the MCP server exposes: search documents, query a customer database, check invoices, read bookings, create a ticket, or trigger an approved workflow.
For example: "Which customers have unpaid invoices?", "What changed in bookings this month?", "Summarize support tickets from enterprise accounts", or "Find the contract and tell me the renewal date." The user writes normal language; the MCP server translates that into safe, validated calls to your data sources.
That is the important part: MCP is not just a chatbot. It is a controlled access layer for company knowledge and operations.
AI becomes valuable when it can use your real systems.
MCP, the Model Context Protocol, is an open standard for connecting AI applications to external systems: local files, databases, APIs, tools, prompts, and business workflows. Read the official MCP introduction.
The business value is simple: instead of rebuilding one-off integrations for every AI app, a custom MCP server gives your team one secure, reusable bridge between natural language and your internal systems.
That bridge needs real engineering. It has to handle authentication, permissions, tool schemas, validation, rate limits, logging, retries, deployment, and safe write actions. That is where we help.
// Tools exposed to approved AI clients server.tool("query_customer", customerSchema, async (input) => { // Scoped database query with audit log return crm.customers.find(input.customerId); }); server.tool("create_invoice", invoiceSchema, async (input) => { // Validated write action behind approval rules return erp.invoices.create(input); }); // Hosts: Claude, ChatGPT, Codex, internal agents // Auth: OAuth + scoped tokens + full traces
What your custom MCP server can expose.
We focus on tools that create measurable impact: faster support, better operations, cleaner reporting, and AI agents that can actually complete work.
Internal APIs
Expose product, billing, logistics, or operations APIs as safe AI-callable tools with strict input schemas.
Databases and analytics
Let AI query Postgres, MySQL, warehouses, or reporting layers without handing it raw credentials.
CRM and support tools
Connect HubSpot, Salesforce, Zendesk, Intercom, Linear, or custom support workflows to AI assistants.
Documents and files
Give AI controlled access to Drive, Notion, local files, PDFs, contracts, manuals, and project documentation.
Write actions
Create tickets, update records, generate invoices, trigger workflows, and require approval for sensitive actions.
Agent tool layers
Build the MCP foundation that autonomous agents use to retrieve context, make decisions, and execute tasks.
How we build MCP servers.
1. Tool discovery.
We map the workflows, systems, permissions, and user roles your AI clients need. We separate read-only context from actions that change data.
2. MCP architecture.
We design the server boundary: transports, auth, tool schemas, validation, secrets, rate limits, logging, and deployment target.
3. Implementation.
We build the MCP server in TypeScript, Python, or the stack that fits your infrastructure, with clean adapters around each internal system.
4. Safety and evaluation.
We test every tool with realistic prompts, malformed inputs, permission failures, and edge cases. Dangerous actions get approval gates and audit trails.
5. Deployment and handover.
We deploy to your environment, connect approved AI clients, document every tool, and train your team to extend the server safely.
Secure by design, not bolted on later.
A custom MCP server can touch sensitive systems, so we design least-privilege access from the first sprint. The AI client never gets broad credentials; it gets narrow tools with validation and observability.
See our engineering processScoped authentication
OAuth, API keys, service accounts, or session-based access with explicit scopes per tool and user role.
Input validation
Strict schemas, typed adapters, allowlists, rate limits, and guardrails before any external call is made.
Approval gates
Human approval for payments, customer-facing messages, destructive actions, or high-impact operations.
Audit logs
Every tool call records who requested it, what data was used, what changed, latency, errors, and cost.
What you get.
Production-ready MCP server connected to your selected systems
Scoped access, input validation, rate limits, and approval rules
Logs, metrics, traces, errors, and cost visibility
Deployment in your infrastructure with environment documentation
Client configuration for Claude, ChatGPT, Codex, or your internal agent stack
Source code ownership, runbooks, and 30-day post-launch support
Typical MCP engagement.
Most custom MCP server builds take 2-5 weeks. We quote fixed-price after a technical discovery call and a short integration audit.
One system, 3-5 tools, read-heavy workflows, basic auth, deployment, and documentation.
Multiple systems, read and write tools, scoped permissions, approval gates, tests, and observability.
Shared MCP platform across teams with governance, complex auth, monitoring, and extensible tool architecture.
All quotes are fixed-price. We can also maintain and extend your MCP server on a monthly retainer after launch.
MCP server development questions.
Do we need MCP if we already have APIs?
Yes, if you want AI clients and agents to use those APIs consistently. MCP wraps APIs as discoverable, schema-validated tools with the context and permissions an AI system needs.
Can an MCP server work with more than Claude?
Yes. MCP is designed as a standard protocol. We can target Claude, ChatGPT, Codex, local agents, internal apps, or any client that supports the protocol.
Can you audit or improve an existing MCP server?
Yes. We review security boundaries, schemas, tool design, error handling, observability, deployment, and client configuration, then ship the fixes.
Ready to build your MCP server?
Book a free technical discovery call. We will identify the safest first tools, estimate effort, and send you a fixed-price proposal.
Book an MCP discovery call