AI Development
MCP Server Development
We build MCP servers that expose safe tool catalogs to AI clients: structured schemas, auth and RBAC alignment, audit logs, and deployment hardening for production use.
Overview
What this service is
We design an MCP tool surface that’s safe by default: explicit capabilities, constrained inputs, and validation so AI clients can’t call arbitrary actions.
Authentication and permissions are aligned to your user model so tools respect RBAC boundaries and produce audit-friendly logs.
We deliver deployment-ready infrastructure with rate limits, secrets isolation, monitoring, and change management so MCP stays stable as tool catalogs evolve.
Benefits
What you get
Safer AI tool access
Constrained tools reduce the risk of unintended actions and data exposure.
Faster integration of new capabilities
Add new tools via schemas and versioned contracts without breaking clients.
Audit-ready operations
Tool calls are logged with context so teams can trace and debug behaviour.
Better governance posture
RBAC alignment and allowlists make approvals and policies easier to enforce.
Production hardening
Rate limits, monitoring, and rollout controls keep MCP stable under growth.
Features
What we deliver
Tool catalog design
Define tool boundaries, schemas, and versions aligned to real workflows and permissions.
Schema validation
Strict input validation and output shaping to make tool use predictable and debuggable.
Auth + RBAC mapping
Integrate with your identity model so tool access is permission-aware and auditable.
Rate limiting and abuse controls
Throttle and protect endpoints to prevent misuse and cost spikes in production.
Observability
Logs, traces, and dashboards for tool usage, failures, and latency across the stack.
Deployment hardening
Secrets isolation, environment separation, and change management for safe operations.
Process
How we work
Tool surface definition
We define tools, schemas, permissions, and versioning strategy for safe capabilities.
Server implementation
We build MCP endpoints, validation, auth integration, and core observability hooks.
Hardening
We add rate limits, secrets isolation, monitoring, and rollback-friendly releases.
Handoff
We document tool schemas, operational playbooks, and safe expansion patterns.
Tech Stack
Technologies we use
Core
Tools
Use Cases
Who this is for
AI copilots for internal tools
Expose controlled actions for dashboards, admin tools, and operations workflows.
CRM automation via safe tools
Let AI clients update CRM records only through validated, allowlisted actions.
Knowledge and search tooling
Expose retrieval and document tools that respect access rules and log usage.
Multi-system orchestration
Coordinate actions across multiple internal services through a stable tool catalog.
Governed AI rollouts
Introduce AI actions behind explicit approvals and audit trails for safer adoption.
FAQ
Frequently asked questions
No. MCP complements APIs by providing a governed tool layer for AI clients. The underlying APIs remain your source of truth.
Yes. We align tool permissions to your RBAC model so access is consistent with your platform security rules.
We use strict schemas, validation, allowlists, approvals for sensitive actions, and rate limits—with audit logs for tracing behaviour.
Yes. We implement secrets isolation, monitoring, and environment separation with operational runbooks.
Yes. Tool catalogs are versioned so you can add capabilities without breaking existing clients.
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Regional
Delivery considerations for your region
Data and risk discovery (Canada)
Privacy, security, residency, and regulatory requirements differ by workflow. We document the applicable data flows, roles, retention needs, and control owners before recommending an architecture.
The resulting proposal lists the controls and evidence that are actually in scope. It is not a generic compliance, certification, or legal-assurance promise.
- Map data sources, destinations, roles, and sensitive fields
- Record access, retention, logging, and deletion requirements
- Identify required security or procurement evidence before contracting
- Use an NDA or DPA only when the parties mutually execute it
Working model (Canada)
Exact live-overlap hours, response expectations, meeting windows, and escalation contacts are confirmed in the proposal for each engagement.
Written decisions, scoped milestones, and asynchronous updates reduce unnecessary meetings without implying an unagreed service level.
- Proposal-specific overlap and meeting windows
- Named owners for decisions and blockers
- Written scope, assumptions, and change decisions
- Milestone cadence agreed before kickoff
Commercial setup (Canada)
The contracting entity, proposal currency, invoicing cadence, payment terms, intellectual-property terms, and required vendor documents are agreed before work begins.
The Opportunity Sprint can establish the evidence needed to scope a production pilot; it does not pre-commit either party to a rollout.
- Contracting entity and currency confirmed in writing
- Milestones and acceptance criteria defined in the proposal
- Vendor-document requirements identified before signature
- Scope changes require an explicit written decision
Delivery controls (Canada)
Testing, observability, release, security, and handover controls are selected for the actual system risk rather than promised as a generic bundle.
Acceptance measures and production responsibilities are recorded before implementation so both teams know what evidence will support release.
- Risk-based testing and acceptance measures
- Release, rollback, and observability responsibilities
- Security controls tied to the agreed threat model
- Handover artifacts defined in the signed scope
Need safe tool access for AI clients?
Share the systems you want to expose—we’ll design an MCP server with secure tooling and a phased rollout plan.
Security-first tool boundaries.