MCP Server Development Services
MCP server development for tools an AI client can use without broad access.
We build Model Context Protocol servers that expose a small, typed set of business tools or resources to compatible AI clients. A focused release covers one client, one underlying system, delegated identity or a bounded service account, read-first tools, confirmation for writes, audit records, failure handling, and compatibility tests.
Bring the problem, the current workflow, or the existing code. We reply with a practical next step within one business day.
Trusted by
The brief
Start with what is not working.
Good software decisions begin with the constraint, not a list of features or a preferred technology.
Does the assistant need live business data, yet the only proposed credential can read or change far more than the task requires?
Can operators see which user, client, model session, tool, arguments, approval, result, and downstream record belong to each call?
Plain answer
MCP server development exposes selected business tools and resources to a compatible AI client through Model Context Protocol. RaftLabs builds bounded servers with typed schemas, delegated identity, least privilege, write confirmation, audit, and failure handling.
What to remember
- Custom MCP servers connect chosen business tools to an approved AI client, bounded by typed schemas and least-privilege access.
- Servers are bounded with typed schemas, delegated identity, least privilege, write confirmation, audit, and failure handling.
- The build starts from what can go wrong: cross-tenant IDs, duplicate retries, and hostile text from connected systems.
The demo could update a customer. It could also update every customer.
The first tool accepted a record ID and free text, then used one service credential with broad write access. The assistant completed the happy path. Nobody had tested cross-tenant IDs, duplicate retries, hostile text returned by the CRM, or the record needed to explain a disputed change.
Focused delivery baseline
- starting first release
- Fixed price
- One client and one underlying system
- typical focused timeline
- Scoped
- Includes adversarial and compatibility tests
- initial capability bias
- Read first
- Writes added only with explicit controls
RaftLabs does not currently publish a named MCP-server outcome case. These figures describe a bounded delivery plan, not promised client compatibility or task completion. Acceptance should measure schema-valid calls, denied access, redaction, pagination, latency, rate limits, duplicate retries, write reconciliation, audit completeness, injected content, client behaviour, and recovery from downstream failure.
A custom MCP server fits when a compatible AI client needs controlled access to proprietary systems.
Start with one client, one system, and a few valuable tools. A connector or direct API call is usually better when the capability is common and fixed.
The assistant or agent must read or change proprietary records through a supported MCP client.
Identity, tenancy, least privilege, sensitive fields, write approval, audit, or failure handling is material.
Business-system, security, AI-platform, and operational owners can approve tool contracts and test cases.
The need is only drafting, summarisation, search, or chat without access to a business system.
A supported connector already provides the required scope, identity, controls, and service level.
The underlying API, record ownership, user identity, approval policy, or security owner remains unresolved.
The first decision is not what to build.
It is whether software is the right answer at all. We use four possible recommendations before discussing a development scope.
Buy
Run the build-vs-buy numbersChoose a maintained product when it handles the core AI tool integrations workflow and the remaining gaps can be solved with configuration, a connector, or a small process change. Paying for software you do not have to maintain is often the better trade.
Repair
Keep the existing product when users still get value from it and the code can be understood safely. Start with evidence: where the code lives, how the data is organized, what is tested, which outside services it depends on, who can access what, and how updates go live.
Build
Own the system when AI tool integrations shape margin, customer experience, speed, or a product customers pay for. Start with one complete path and the system connections that path cannot work without.
Wait
Do not turn a changing policy into permanent software. Stabilise the rules, name the workflow owner, and measure the cost of the current process before paying for a product.
Not sure which path is yours? Bring it to a 30-minute call. Leave with the answer in writing.
Separate the protocol layer from the AI system
| Need | Best fit | Boundary |
|---|---|---|
| Expose selected tools to compatible AI clients | MCP server | Tool schema, client support, identity, access, confirmation, audit, and errors |
| Provide a deterministic interface to business logic | API development | Endpoints, contracts, authentication, versioning, reliability, and consumers |
| Plan and complete a goal with tools | AI agent development | Model, state, decisions, tool selection, human gates, and outcome |
| Coordinate several model or tool steps | AI orchestration | Sequence, routing, state, retries, fallbacks, and review |
| Add model features to an existing product | LLM integration | Use case, provider, prompts, context, evaluation, and operation |
Scope
What belongs in a focused MCP server
Typed tool and resource contracts
Give each capability one narrow job, explicit inputs, bounded output, clear descriptions, stable identifiers, pagination, errors, and examples that match the underlying business API.
Identity and least privilege
Authenticate the client, preserve the end-user or service identity, enforce tenant and row scope, separate roles, minimise credentials, and deny unapproved fields or operations.
Write confirmation and idempotency
Preview material changes, require the agreed approval signal, cap batch size, control duplicate retries, preserve downstream identifiers, and reconcile the final business record.
Untrusted-content and output controls
Treat tool results and resource content as data rather than instructions. Filter sensitive fields, bound result size, validate output, and keep injected text away from policy decisions.
Observability and client compatibility
Trace calls, identities, arguments, approvals, timing, results, errors, and side effects. Test the named clients and protocol features, then record supported versions and known gaps.
How it works
From business capability to accepted MCP tool contract
- Phase 101
Define client, system, identity, and tools
Choose the supported client, underlying system, user roles, read and write jobs, resource boundary, owners, approvals, measures, limits, and acceptance examples.
- Phase 202
Prove access and threat boundaries
Verify APIs, schemas, credentials, delegated identity, tenancy, sensitive fields, untrusted content, rate limits, errors, client behaviour, and downstream side effects.
- Phase 303
The bounded MCP server, set up
Create typed tools or resources, validation, least privilege, confirmation, idempotency, redaction, pagination, audit, tracing, recovery, and compatibility tests.
- Phase 404
Test clients and hand over
Run accepted and adversarial calls, reconcile writes, rehearse denial and outages, document version support, train owners, and release after security approval.
Risk
What the MCP specification must settle
- Client and version support
- Name transports, features, authentication, approval behaviour, deployment, and versions in scope. Retest compatibility when the client or protocol changes.
- Delegated authority
- Decide whether the server acts as the user or a service, how tenant and record access is enforced, and which system remains authoritative for permission.
- Prompt injection through data
- Treat content returned by tools or resources as untrusted. Keep it from redefining tool policy, expanding access, or bypassing write approval.
- Side effects and retries
- Define preview, approval, idempotency, batch limits, timeouts, partial success, rollback, reconciliation, and the person who resolves a disputed write.
Scope and price
A focused MCP server, priced before development starts.
Begin with one supported client, one system, a bounded tool set, explicit identity, read-first access, audit, compatibility tests, and an operating owner.
A broader MCP estate adds scope. Use an existing connector when its access, tools, compatibility, and support already fit.
Starting investment
Starts at $10000
Several clients, systems, tenants, write paths, legacy APIs, or formal security reviews extend the plan. After a 30-minute call, you get a written build, buy, repair, or wait recommendation plus first-phase direction. Free, whether or not you build with us.
A protocol does not grant authority
Compatibility is named and tested
Related AI integration services
- 01
AI Agent Development
Build goal-directed systems that decide when and how to use tools.
- 02
AI Orchestration
Coordinate model and tool steps with state, retries, fallbacks, and review.
- 03
LLM Integration
Add evaluated model features to an existing product or workflow.
- 04
API Development
Build deterministic interfaces and business contracts beneath AI tools.
Work with us
Bring the business tools the AI client should and should not reach.
Share the client, user roles, underlying systems, APIs, read and write jobs, sensitive fields, approval rules, audit needs, and security owner. We will define a bounded server.
- Scope and cost agreed before work starts. No surprises. No obligation.
- Working prototype within 3 weeks of kickoff.
- Pay by milestone. You see progress before each invoice.
- 60-day post-launch warranty. Bug fixes, UI tweaks, and deployment support. No retainer.
- All conversations are NDA-protected.
Common questions
An MCP server exposes selected tools, resources, or other supported capabilities to a compatible AI client through Model Context Protocol. The server sits between the client and underlying systems. It should validate inputs, enforce identity and access, control outputs and side effects, record calls, and handle failures.
An API is the deterministic business interface the MCP server often calls. The MCP server describes selected capabilities in a form compatible AI clients can discover and invoke. An agent decides when to use tools and may coordinate several steps. MCP does not replace the API, model, workflow policy, or agent runtime.
Not automatically. Clients can differ in supported protocol features, transports, authentication, user interface, approval behaviour, limits, and release cadence. We name the client versions and capabilities in scope, test them directly, and document gaps. Compatibility with an untested future client is not a delivery guarantee.
Start with narrow inputs and permissions. Separate read from write, show a preview, require an approved confirmation for material changes, use idempotency to control retries, record before and after state where appropriate, cap volume, expose errors, and keep destructive or regulated decisions under an accountable human or system.
The agent acts only on behalf of the authenticated user, never a master service account, otherwise cross-tenant data exposure. Prioritize audit logs plus correlation IDs, and short-lived credentials per tool call. Least privilege and auditing are hard once any function can become a tool.
Start with a bounded, fixed-scope pilot before rolling out enterprise-wide access. Draft a strict identity policy before shortlisting agencies. Destructive operations need human approval: the agent may draft, the human clicks approve.
The specification changes constantly and every update can potentially break the integration. The safety story, idempotency, reversibility, intent preview, is the real denominator. Per-client cost compounds, so multi-client support multiplies the surface. Pilot, not promise.
A focused release covers one supported client, one underlying system, a bounded tool set, authentication, access control, confirmation, audit, failure handling, compatibility tests, deployment, and handover. Several clients, systems, tenants, write paths, or security reviews increase scope. Every release is priced at a fixed cost agreed before development starts.