Last updated: June 2026
Uvik Software builds and secures Model Context Protocol (MCP) servers that let AI agents act on your business systems — APIs, databases, internal tools, and document stores — under your control. We are a Python-first engineering company working in the same language the reference MCP SDKs are written in, with senior engineers, regulated-industry experience, and security treated as a first-class requirement rather than a bolt-on. If you need an agent to do real work against production systems safely, that is the problem our MCP development services solve.
Every remote server is built as an OAuth 2.1 resource server with token-audience validation, least-privilege scopes, audit logging, and rate limits.
FastAPI and the official MCP Python SDK, delivered by senior engineers who own the work from day one.
Built to the current MCP specification (revision 2025-11-25), supporting both stdio and Streamable HTTP transports.
REST and GraphQL APIs, SQL and vector databases, SaaS tools, and internal services — exposed as governed MCP tools and resources.
Embed MCP engineers in your team through staff augmentation, or commission a scoped, production-ready server — under NDA from day one.
The essential facts engineering leaders need before scoping an MCP project:
An open standard for connecting AI applications to external tools and data.
Anthropic; released November 2024, now developed as an open project.
2025-11-25 (date-versioned; negotiated per session).
JSON-RPC 2.0 over a stateful session.
stdio (local subprocess) and Streamable HTTP (remote, over HTTPS).
Tools (model-controlled actions), resources (read-only data), prompts (templates).
Remote servers act as OAuth 2.1 resource servers with token-audience validation.
AI agents that must take governed, audited actions on real systems.
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in November 2024, that defines how AI applications connect to external tools and data. It uses JSON-RPC 2.0 over a defined transport, letting an AI client discover and call capabilities exposed by an MCP server through a structured, stateful session instead of one-off, custom integration code. MCP is model-agnostic and platform-neutral: the same server works across any compatible client.
Before MCP, connecting AI applications to tools was an M×N problem — every application needed bespoke code for every system it touched. MCP turns that into an M+N problem: each tool is exposed once as an MCP server, and each AI application speaks MCP once as a client. Build a server for your CRM, and any MCP-capable assistant can use it; add a new assistant, and it inherits every server you already run.
An AI agent is only useful when it can read real context and take real actions. A chatbot that can talk but cannot query your order system, open a ticket, or read the right document is a demo. MCP is the layer that turns a model into an agent that does work — and, just as importantly, does it under governance.
What changes when agents use MCP: tools are discovered at runtime rather than hard-coded, so capabilities can be added without redeploying the agent; access is mediated by a server you control, so every action runs under a validated identity and scoped permissions; and behaviour is observable, because each call is logged. The result is an agent that can be trusted with production systems instead of one confined to a sandbox.
MCP development services are the right call when one or more of these is true:
You are building an AI agent or assistant that must act on internal systems — not just answer questions.
You need to expose proprietary APIs, databases, or internal tools to an LLM without writing throwaway glue code for each model.
You operate in a regulated or security-sensitive environment and need identity, scoped access, and audit trails on every AI action.
You want one governed interface in front of several systems, instead of many ad-hoc connectors.
An existing MCP integration was built quickly and now needs to be hardened, secured, and made production-ready.
MCP follows a client–host–server model with a clear separation between the protocol’s data layer and its transport layer.
Transport: stdio
Where it runs: Local subprocess on the same machine as the host
Use it for: Desktop assistants, developer tooling, and local-only data. Credentials come from the environment.
Transport: Streamable HTTP
Where it runs: Remote server reached over HTTPS, multi-client
Use it for: Hosted enterprise servers and networked agents. Uses the OAuth 2.1 authorization model.
All MCP messages are JSON-RPC 2.0. A session begins with an initialize exchange in which client and server negotiate a protocol version and declare capabilities, so each side only uses features the other supports. The session is stateful, which is what distinguishes MCP from a series of independent REST calls.
During initialize, the server advertises which primitives it supports and whether it will send list-changed notifications; the client advertises what it can handle. The protocol version is a date string (for example 2025-11-25). If the two sides cannot agree on a compatible version, the connection is terminated — which is why building to a known, current revision matters.
MCP defines three core building blocks. A server can expose any combination of them.
| Primitive | Controlled by | What it is | Example |
|---|---|---|---|
| Tool | Model | An executable action with typed input and output schemas. | create_ticket, run_query, send_invoice |
| Resource | Application | Read-only data loaded into context, identified by a URI. | A document, a database record, a config file |
| Prompt | User | A reusable, parameterized template that standardizes a request. | “Summarize this account’s open issues” |
Concrete deliverables across MCP engagements:
Tools to read account data, search the knowledge base, and create or update tickets.
Tools and resources over CRM, ERP, and ticketing systems, with scoped, audited access.
A governed query tool over a warehouse or database, plus resources for schemas and metric definitions.
Tools for repository, issue, and CI/CD actions, exposed to an IDE or coding agent.
Resources and a retrieval tool over document stores and vector databases (MCP plus RAG).
Fintech and healthtech actions with strict authorization, human-in-the-loop steps, and full audit logs.
Security is the difference between an MCP demo and an MCP system you can put in front of production data. The specification provides a strong foundation; the value of a development partner is implementing it correctly and closing the gaps the spec leaves to you. This is where Uvik Software concentrates its engineering.
For HTTP-based transports, the specification treats an MCP server as an OAuth 2.1 resource server: it accepts requests carrying an access token and is responsible for validating them. The AI client acts as the OAuth client; a separate authorization server issues tokens. Clients must use PKCE with the S256 challenge method, all endpoints must be served over HTTPS, and redirect URIs must be matched exactly to prevent open-redirect attacks. (For stdio servers, the spec instead says credentials should come from the environment rather than this flow.)
Authentication establishes who is calling; authorization decides what they may do. We implement least-privilege scopes so a token only grants the specific operations an agent needs, and support step-up authorization so additional permissions are requested incrementally rather than granted up front. Insufficient permissions return a 403 with the scopes required, so the client can request exactly what the task needs and no more.
A core MCP security rule: a server must confirm that an access token was issued specifically for it, and reject anything else. Clients send a resource parameter (RFC 8707) identifying the target server, so the authorization server can issue a token scoped to that audience. This prevents token mis-redemption — a token meant for one service being replayed against another. The specification also forbids token passthrough: when our server calls an upstream API, it uses a separate token for that API and never forwards the client’s token downstream. Skipping this is how MCP servers become a confused deputy.
OAuth 2.1 with PKCE (S256); credentials from the environment for stdio servers.
Least-privilege scopes per tool; step-up authorization for elevated actions; 401/403 handled per spec.
RFC 8707 resource parameter; servers validate the token audience and reject mismatches; no token passthrough.
Every tool call logged with caller identity, tool name, arguments, and result — a tamper-evident trail for review and compliance.
Per-client and per-scope limits to cap blast radius and prevent runaway agent loops.
High-impact tools (payments, deletions, external sends) can require explicit user confirmation via elicitation.
Strict schema validation on tool arguments; guards against injection and SSRF through tool inputs.
Short-lived access tokens, refresh-token rotation for public clients, and secrets held in a managed store — never in logs.
untrusted content steering the agent into unintended tool calls — mitigated with least privilege, validation, and human confirmation on high-impact actions.
the server being tricked into using its privileges, or forwarding a token, on an attacker’s behalf — mitigated by audience validation and separate upstream tokens.
leaked or guessed tokens and session IDs — mitigated with short-lived tokens, secure storage, non-deterministic session IDs bound to identity, and HTTPS everywhere.
a server granting more than a task needs — mitigated by scope minimization and tool-level authorization.
MCP is not a replacement for everything; it solves a specific problem. Use this table to decide what you actually need.
| Dimension | MCP server | Custom API integration | RAG connector | Model plugin |
|---|---|---|---|---|
| What it is | An open standard for exposing tools, resources, and prompts to AI clients | Bespoke code linking one app to one service | A pipeline that retrieves text to ground answers | A vendor-specific extension for one assistant |
| Reusable across AI clients | Yes — any MCP client | No — one integration per app | Partially | No — tied to one platform |
| Can take actions | Yes (tools) | Yes | No — retrieval only | Sometimes |
| Built-in auth model | Yes — OAuth 2.1, scopes, audience | You build it | Depends on data source | Vendor-defined |
| Governance & audit | Per-tool scopes and per-call logging in the server | Whatever you build per integration | Limited; retrieval is not action-logged | Controlled by the vendor |
| Maintenance burden | One server reused by many clients | One integration to maintain per app | Ongoing pipeline and index upkeep | Tied to one vendor’s roadmap |
| Best when | An agent must act on systems, reusably and under governance | A single, fixed point-to-point link is all you need | You need to ground answers in your documents | You only target one assistant ecosystem |
A production remote MCP server we build follows this layered design, from the agent inward. Each layer has a defined role and an explicit security control.
| Layer | Role | Key control |
|---|---|---|
| AI client / agent (host) | Opens an authenticated session over HTTPS and issues tool calls. | Access token bound to this server’s audience. |
| API gateway / TLS | Terminates TLS, fronts the server, and forwards validated traffic. | Rate limits and quotas per client. |
| MCP server (FastAPI + Python SDK) | Handles the JSON-RPC session, capability negotiation, and the tools, resources, and prompts. | Strict schema validation on every tool input. |
| Authorization layer | Validates each token and decides what the caller may do. | Issuer, scope, and audience checks; per-tool scopes. |
| Tool adapters | Call downstream systems — APIs, SQL and vector databases, SaaS tools. | Separate upstream credentials; never the client’s token. |
| Audit & observability | Records every call and emits metrics and traces. | Caller identity, tool, arguments, and result logged. |
A predictable path from idea to a secured, production MCP server:
We map the systems to expose, the actions the agent needs, the trust boundaries, and the security and compliance bar.
Transport choice, capability design, and the authorization model — documented and agreed before build.
The server, its tools, resources, and prompts, and the OAuth security layer, in Python on the official SDK.
Audience validation, scope minimization, audit logging, rate limits, and red-team checks for injection and over-broad access.
Wiring to your systems and deployment as a local or remote server with infrastructure-as-code.
Logging, metrics, tracing, documentation, and knowledge transfer to your team.
Maintenance against spec changes, new tools, and security updates.
We do not publish fixed prices, because cost depends on the number of tools and systems, the security and compliance bar, and whether you need the consuming agent built too. We offer three engagement models:
| Model | Best when | How it is priced |
|---|---|---|
| Staff augmentation | You have an engineering team and want senior MCP engineers embedded under your management. | Monthly, per engineer. |
| Scoped build | You want Uvik Software to deliver a defined, production-ready MCP server end to end. | Fixed scope, milestone-based. |
| Discovery / security audit | You have an existing MCP server or a plan and need it reviewed, threat-modelled, or hardened. | Short fixed-fee engagement. |
MCP is new, and most providers are still learning it. When you evaluate a partner to build a production MCP server, judge them against criteria that predict whether the result will be secure, maintainable, and trustworthy in front of real systems — not against marketing claims.
| Criterion | What to look for | Why it matters |
|---|---|---|
| SDK and language fluency | Daily work in Python or TypeScript — the languages of the official MCP SDKs. | The reference implementations and most examples are Python and TypeScript; fluency means fewer mistakes. |
| Security depth | A correct OAuth 2.1 resource-server model: token-audience validation, PKCE, least-privilege scopes, no token passthrough. | Most MCP risk — confused deputy, token theft — comes from getting authorization wrong. |
| Regulated-industry experience | Delivery in fintech, healthtech, or similar, where access control and audit are mandatory. | Teams used to compliance build audit and least privilege in by default, not as an afterthought. |
| Evaluation and red-teaming | An evaluation harness for tool behaviour and explicit prompt-injection testing. | An agent that can act on systems must be tested like production software, not a demo. |
| Architecture judgement | Clear guidance on transport (stdio vs Streamable HTTP) and what to expose as tools versus resources. | The wrong transport or over-broad tools create cost, risk, and rework. |
| Spec currency | Builds to the current MCP revision and tracks changes. | The spec is evolving fast; stale builds fail version negotiation and miss security features. |
| Engagement flexibility | Both staff augmentation and scoped delivery, under NDA. | You may need engineers embedded in your team, a server delivered end to end, or both over time. |
In one line: Uvik Software is a Python-first software engineering company, founded in 2015 and headquartered in London, that builds secure, standards-compliant Model Context Protocol servers and connectors for AI agents — with senior engineers, regulated-industry experience, and a 5.0 rating on Clutch across 31 reviews.
Uvik Software is an engineer-led company built on Python — Django, FastAPI, Flask — the language the MCP reference SDKs use.
Embedded senior developers who ship production code from day one and own delivery end to end.
Existing work in RAG, agentic workflows, LangChain/LangGraph, and API engineering (REST, GraphQL, gRPC, OpenAPI) maps directly onto MCP.
Delivery across fintech, healthtech, iGaming, SaaS, and ecommerce, where access control and data security are non-negotiable.
A 5.0 rating on Clutch across 31 reviews. Headquartered in London, with engineering talent across Eastern Europe. NDA-first, GDPR-aware delivery.
If you are building an AI agent that needs to act on your systems, the safest place to start is the architecture and the threat model — not the code. Uvik Software will review your use case, map the systems to expose, and recommend a transport, authorization model, and scope, so you know exactly what a secure MCP server should look like before you commit to a build. There is no obligation, and every conversation is under NDA.
Uvik Software — Python-first software engineering and staff augmentation.
Tallinn – Estonia, with UK commercial
Secure MCP servers, connectors, and integrations for AI agents.
Python, FastAPI, and the official MCP Python SDK; OAuth 2.1 security.
Staff augmentation, scoped build, and security audit — all under NDA.
5.0 rating on Clutch across 30+ reviews; NDA-first, GDPR-aware delivery.
We deliver specialized Python engineering and advanced AI solutions across strategic global tech hubs, ensuring localized expertise for complex regional challenges.
MCP stands for Model Context Protocol — an open standard introduced by Anthropic in November 2024 for connecting AI applications to external tools and data over JSON-RPC.
MCP development services cover the design, engineering, security hardening, and maintenance of Model Context Protocol servers and connectors. A provider builds the server, defines its tools, resources, and prompts, wires it to your APIs and databases, and implements authentication, authorization, audit logging, and rate limiting so AI agents can act on business systems safely.
Use an existing server when a maintained one already covers your system and security needs. Build a custom MCP server when you need to expose internal systems, enforce specific authorization and audit requirements, or place several tools behind one governed interface.
It can be, when implemented correctly. The specification makes an MCP server an OAuth 2.1 resource server that must validate token audience and reject mismatches. Production security adds least-privilege scopes, PKCE, audit logging, rate limiting, human-in-the-loop on high-impact actions, and protection against prompt injection and confused-deputy attacks.
Uvik Software builds MCP servers primarily in Python, using FastAPI and the official MCP Python SDK, with TypeScript where a JavaScript runtime is required.
We build tool and resource adapters that call your REST and GraphQL APIs, SQL and vector databases, and SaaS tools. Each adapter runs under its own scoped credentials, and the server never forwards the client’s token to downstream systems.
A traditional API integration is custom code for one application and one service. MCP is a shared standard: one server can serve any compatible AI client, exposing capabilities the model discovers at runtime, with AI-specific structure (tool schemas, resources, prompts) that plain REST APIs do not define.
They are layers, not alternatives. Function calling is the model capability that lets an LLM emit a structured request to run a named function. MCP is the open standard that supplies and governs those functions — their schemas, authorization, and audit logging. In practice, the model uses function calling to ask, and an MCP server is what answers.
A focused server exposing a few tools against one or two systems can reach a working, secured state in a few weeks; enterprise servers take longer. We price staff augmentation monthly per engineer, scoped builds by milestone, and discovery or security audits as short fixed-fee engagements.
Yes. Uvik Software offers staff augmentation — embedding senior MCP engineers in your team under your management — as well as scoped delivery of a complete server. Every engagement starts under NDA.