Custom MCP Servers
Design and build MCP servers that expose your databases, CRMs, internal APIs, and document sources to LLM applications in a controlled way.
Model Context Protocol · Manchester
MCP systems that give AI agents real business context
We design and build Model Context Protocol systems for Manchester and UK businesses that need agents to work with live tools, data, and workflows instead of operating in a sandbox of guesswork.
Scoped tool access · Audited every call · Production from day one
What we build
MCP servers, connectors, and agent workflows
MCP is the connective layer between powerful models and the systems your team actually uses. We focus on practical architecture, security, and production reliability.
Secure Tool Connectors
Connect Claude, OpenAI, and internal agents to live systems with scoped permissions, audit trails, and sensible guardrails around every tool call.
Agent Workflows
Build multi-step workflows where AI agents can inspect data, call business tools, and hand work back to humans without leaking context or overreaching.
Governance & Access Control
Enforce least-privilege access, tenant boundaries, structured logging, and project-level isolation so your AI systems can operate safely in production.
Use cases
Where MCP earns its place
Real operational work for Manchester and UK teams — not demos. Agents that read live data and complete tasks across the systems you already run.
Sales & RevOps
Let agents read CRM records, generate account briefs, draft follow-up actions, and update pipeline notes without manual copy-paste between systems.
Knowledge Operations
Connect document stores, wikis, support content, and SQL sources so agents can answer with live business context instead of stale prompt context.
Ops Automation
Trigger workflows across ticketing, scheduling, onboarding, analytics, and internal operations through MCP-connected tools and approval checkpoints.
The process
How we deliver MCP projects
We do not ship agent tooling as a demo. We build it as operational infrastructure with the right boundaries, observability, and rollback thinking from day one.
01
Discovery workshop
We map systems, permissions, and real operational use cases for your Manchester team.
02
MCP architecture
We design the architecture for your tools, data stores, and approval boundaries.
03
Implementation
We build the server and connectors in TypeScript or Python against your stack.
04
Test & harden
We test against realistic agent tasks and failure scenarios before go-live.
05
Deploy & monitor
We deploy with monitoring and iterative hardening for production use.
Good fit for MCP
Is MCP right for your team?
MCP is powerful when you already run real systems and want AI to operate inside them safely. You are a strong fit if any of these sound like your Manchester business.
Teams already running multiple internal systems and APIs
Businesses that want AI agents to work on live operational tasks
Environments where auditability and scoped access matter
Products that need tool use, retrieval, and action in one workflow
Who Needs an MCP System Rather Than a Chatbot
A chatbot answers from what it was given. An MCP system lets an AI model reach into the tools your business runs, look up what is true right now, and take an approved action. That extra power only pays off when there is real work for the agent to do inside those systems, and a clear idea of what it must never touch.
The typical starting point is a team that has tried an AI assistant, found it useful for drafting, and now keeps copying data in and out of it by hand. Whether you are a Manchester operations team or a product company elsewhere in the UK, the question is the same: which repeated tasks could an agent finish if it could safely see the right records?
When it is not the right fit: If you only need one model to call one API, a direct function call is simpler and cheaper to maintain. If the underlying data is unreliable, connect nothing yet: an agent will repeat bad data confidently. And if you only want a single server with a known scope, our MCP server development service is the more direct route.
Staff copy data between AI and business tools
If people paste CRM records, tickets or reports into an assistant all day, connecting those sources directly removes the slowest step.
The data lives in systems with an API or database
CRMs, ticketing tools, SQL databases and document stores are straightforward to expose as scoped tools. Systems with no way in need more work first.
You can name the risky actions
Knowing which actions need a human to approve them is what makes a safe design possible, so we ask early.
More than one AI client will use the tools
Because MCP is a shared protocol, one well-built server can serve several assistants and agents, instead of one custom integration per model.
What Each MCP Delivery Stage Produces
The five delivery stages above each leave something you can check. This is what you should expect to have in your hands at each point, before you commit to the next.
- 01
A task list, not a wish list
Real tasks written down with the systems each one touches and how you would know it was done correctly. These become the test cases later.
- 02
A permissions map
For each tool: who can use it, what it can read, what it can change and which actions need approval. This is the document your security reviewer will want.
- 03
Tool definitions
Small, clearly named tools with validated inputs and outputs. Fewer precise tools work better than many vague ones, because the model has to choose between them.
- 04
Failure testing results
What happens when an API times out, a record is missing, or an instruction hidden in a document tries to steer the agent. These cases get tested before go-live.
- 05
Logs you can read
Each tool call recorded with its inputs, its result and who it acted for, so you can answer what the agent did and why after the event.
What Shapes the Cost of an MCP Project
MCP work uses our standard model: a fixed-price discovery sprint, then a fixed-scope build, with an optional monthly partnership. Model and hosting costs are billed at cost on your own accounts. The build price mainly depends on these factors.
See our AI development pricing- Number of systems connected
- Each system has its own API, sign-in and edge cases. A modern REST API is quick; an old database or an undocumented back-office tool takes longer.
- Read-only or read-write
- Reading data is much simpler than changing it. Write actions need validation, approval steps and a way to undo or report what happened.
- Identity and tenancy
- Acting for individual users with their own permissions, or keeping several clients' data apart, is more work than a single shared service account.
- Audit and compliance needs
- Retention rules, regulated data and formal security review all add design and testing time, so we scope them up front.
How We Judge an MCP System
A demo that works once proves very little. Models choose tools differently from one run to the next, so we test the written task list repeatedly and look at how often the agent reaches the right result, not whether it managed it on one good attempt.
Security gets the same treatment. Content an agent reads can contain instructions, so anything it retrieves is treated as untrusted data, and the permissions map rather than the model decides what is allowed. We will not tell you an agent is perfectly safe. We will show you what it can reach, what stops it, and the log of what it did.
- Task success rate across repeated runs of the agreed task list
- How often the agent picks the wrong tool or wrong inputs
- Actions blocked by permissions, and whether each block was correct
- Time per task against doing it by hand
- Share of agent actions your staff approve without changes
Related Services and Agent Work
MCP is one layer of an agent system. These services often sit alongside it, and Aurora Assistant, our own product, shows the same design problems in practice: choosing the right tool, recovering when a service fails, and confirming before anything that matters.
MCP server development A single custom server connecting Claude or ChatGPT to your databases, APIs and internal tools.OpenAI API integration GPT and Claude APIs built into your own product, with or without an MCP layer.Aurora Assistant: an agent that acts One assistant routing requests across banking, travel, smart home, calls and email, with confirmation first.
Related services
MCP Systems FAQs
Straight answers on how Model Context Protocol projects work in practice.
MCP, or Model Context Protocol, is the layer that lets an AI system use real tools instead of only answering from its prompt. In practical terms, that means your assistant can look up a customer record, inspect a ticket, search internal knowledge, or trigger a business workflow in a structured way.
A standard chatbot is mainly a language interface. MCP systems turn that interface into something operational by connecting the model to trusted data and approved actions. That is the difference between a chatbot that sounds useful and an agent workflow that actually completes work.
Yes. We can build connectors for internal APIs, PostgreSQL, CRMs, ticketing systems, knowledge bases, document stores, and bespoke back-office tools. The goal is to fit MCP around your existing systems rather than forcing a platform rewrite.
We scope tools tightly, validate every input and output, enforce tenant and project isolation, log tool activity, and add approval steps where actions carry risk. MCP is powerful, so the implementation has to be disciplined from the start.
Focused MCP connector work starts from a discovery and prototype engagement, while larger multi-tool agent systems depend on the number of systems, permissions, and operational workflows involved. We scope against business outcomes, not vague experimentation.
MCP is an open protocol, and clients such as Claude support it directly. Other model providers and agent frameworks have added support too, though the level varies. We check the exact clients you plan to use during discovery, and where one does not support MCP we can expose the same tools through a normal API.
It can try to be. Text inside an email or document can contain instructions aimed at the agent, which is known as prompt injection. No model is fully immune, so protection comes from the design: narrow permissions, validated inputs and human approval on risky actions, so a fooled agent still cannot do serious harm.
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