MCP server development means building Model Context Protocol servers that give AI models and agents controlled access to your ERP, CRM, ticketing and document systems, with scoped permissions, identity-based authentication and audit logs, so every agent connects through one governed layer.
Book discovery callEvery pilot writes custom code to reach Salesforce, SAP or ServiceNow, so you end up with duplicated connectors and inconsistent security.
Shared service accounts give an assistant full read-write rights when the use case only needs to look up an order status.
Calls to business systems from AI tools aren’t logged in one place, so audit and incident response have nothing to work from.
Community connectors help in demos, but they rarely match your data model, identity provider or change control process.
We map which systems, objects and actions your AI use cases actually need, then design MCP tools around business tasks rather than raw API endpoints. Fewer, well-described tools help models pick the right action and reduce wasted calls.
We build MCP servers in TypeScript or Python for your ERP, CRM, ticketing, document and database systems. Each exposes typed tools, resources and prompts, with input validation and error messages written so models can recover from mistakes.
Servers act as OAuth 2.1 resource servers that validate tokens from your identity provider, such as Entra ID or Okta. Agents act on behalf of a real user or a scoped service identity, never through a shared admin account.
Read-only tools, drafting tools and write actions get different controls. Writes that move money, change records or message customers can require human confirmation, and business rules stay in deterministic code the model can’t override.
A central gateway registers approved servers, enforces rate limits and logs every tool call with caller, inputs, outputs and timing. Logs flow to your SIEM, so security teams review agent activity alongside everything else.
We test each server with contract tests against sandbox systems and with model-driven evaluations that check agents choose the right tools. Regression runs catch breaking changes when an upstream API or model version changes.
Servers run as containers in your cloud or on-premises environment over streamable HTTP, behind your firewall and existing change control. Infrastructure as code and CI pipelines make the layer reproducible across environments.
Once several servers exist, we help you organise them into an internal agentic platform: a server registry, shared policies, observability and templates so new teams connect agents to approved tools without writing new integrations.
Bring your use case list and we’ll map the tools and permissions each one needs.
Plan your MCP layerWe review your target AI use cases, systems of record and identity setup with IT and security. We agree on the first servers, tool definitions and permission tiers.
Deliverables: integration inventory, tool specifications, permission matrix, security design
We build and test MCP servers for the priority systems against sandbox environments. Agents from your pilot use cases call them end to end before anything touches production data.
Deliverables: MCP servers, contract tests, agent evaluations, sandbox deployment
We add the gateway, logging, rate limits and SIEM integration, then pass your security review. Servers go live in your environment through your change control.
Deliverables: production deployment, gateway configuration, audit logging, security review sign-off
Your engineers build the next server using our templates while we review. We document how new teams register servers and request access.
Deliverables: server templates, registry, runbooks, trained team
One MCP server per system serves every compatible model and agent, replacing per-project connector code.
Token-based identity and permission tiers limit each agent to the objects and actions its use case requires.
A central gateway logs each tool call to your SIEM with caller, inputs and results attached.
Because MCP is supported across major model providers, changing models doesn’t mean rebuilding integrations.
A registry and server templates mean new AI use cases start from approved tools instead of new code.
Ready to see this in your own workflows?
Book discovery callThe right choice depends on how many AI use cases you expect and how tightly you need to govern access.
| Criterion | Point-to-point custom code | Hosted MCP connector platform | tkxel FDE pod build |
|---|---|---|---|
| Reuse across agents | None, per project | High for supported apps | High, your own registry |
| Fit to your data model | Exact, but duplicated | Generic objects and actions | Designed around your tasks |
| Identity and permissions | Varies by team | Platform-defined scopes | Your IdP, risk-tiered tools |
| Runs inside your network | Yes | Usually vendor-hosted | Yes, your environment |
| Upfront effort | Low for one use case | Low, subscription setup | Moderate, several weeks |
| Fails when | Use cases multiply | Systems are custom or on-premises | Only one use case exists |
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
“Placeholder quote — replace with a real client quote.”
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
“Placeholder testimonial — replace with the global set from tkxel.com.”
“Placeholder testimonial — replace with the global set from tkxel.com.”
MCP server development is building servers that implement the Model Context Protocol, an open standard that lets AI models and agents call tools and read data from external systems in a consistent way. Each server exposes a system, such as your CRM or ERP, as a set of described tools and resources. Any MCP-compatible client, including assistants and custom agents, can then use those tools without its own custom integration, subject to the authentication and permissions you define.
MCP lets you build an integration once and reuse it across every compatible model and agent, while direct API integrations are rebuilt for each project. Anthropic introduced MCP as an open standard in November 2024, and other major model providers have since adopted it, which reduces lock-in to one vendor. Direct API code still makes sense for a single fixed automation with no model in the loop; MCP pays off when several AI use cases need the same systems.
MCP can be run securely, but the protocol alone doesn’t make it so; security depends on how each server is implemented and deployed. We validate OAuth 2.1 tokens from your identity provider, scope tools by risk, keep business rules in deterministic code, require confirmation for consequential writes and log every call to your SIEM. We also review third-party servers before approval, because an unvetted server can expose data or carry malicious tool descriptions.
We can build MCP servers for any system with an API, database or reliable export, including SAP, Oracle, Microsoft Dynamics, Salesforce, HubSpot, ServiceNow, Jira, Zendesk, SharePoint and internal applications. Where an official or community server already exists, we assess whether it meets your identity, logging and data model requirements before building our own. Legacy systems without APIs usually need an integration service in front of them first, which adds time to the first step.
Start with a few MCP servers tied to real use cases, then add platform pieces when more teams need them. A registry, shared policies, a gateway and templates are worth building once several teams are connecting agents to the same systems. Building a full agentic platform before any use case is in production often produces infrastructure nobody uses. The trade-off is some rework later, which we reduce by designing the first servers to fit a gateway from day one.
A tkxel FDE pod typically delivers the first production MCP servers in about 7 to 11 weeks, including security review. Design takes one to two weeks, building and testing against sandbox systems three to four, hardening and deployment two to three, and handover one to two. Additional servers go faster once the gateway, logging and templates exist. Waiting for sandbox access and security review slots is the most common source of delay.
Yes, any client that supports the Model Context Protocol can use the same server, including Claude, ChatGPT, Microsoft Copilot Studio and agents built with common frameworks. Client support for specific features, such as remote servers, OAuth flows or resources, still differs between products, so we test your servers against the clients you actually plan to use. That keeps your integrations independent of which model or assistant your organisation chooses next year.