Approved tool inventory
Every tool, resource, and prompt listed with inputs, outputs, owner, and the permission it needs.
The Problem
Your AI Assistant Can Talk, but It Can't Reach Your Systems
It shows up the week someone asks Claude or ChatGPT to check an order or open a ticket. The answer lives in your systems, and the shortcut on offer is an admin key in a config file.
Admin keys
One shared API key gives the assistant full admin rights.
Admin keys
OAuth sign-in runs each call with the signed-in person's own permissions.
Duplicate plugins
The same integration gets rebuilt for every AI tool.
Duplicate plugins
One server works with Claude, ChatGPT, Cursor, and any compliant client.
Wrong tool
Vague tool descriptions make the model pick the wrong action.
Wrong tool
Tools get clear names, typed inputs, and descriptions tested on real requests.
Audit trail
Nobody can say who triggered a delete, or why.
Audit trail
Every call is logged with the user, inputs, result, and time.
Your team wants the assistant to answer from the CRM, file a Jira ticket, and look up an invoice. Each AI tool wants its own plugin, so the same integration gets built three times. A developer wires up a quick local server with an admin API key saved in a JSON file on a laptop. Now any prompt that reaches the model can trigger a delete, and nobody can say which person made the call. Tool descriptions are vague, so the model picks the wrong action on simple requests. Then security asks the question nobody can answer: what can this thing actually touch?
We start with a tool inventory: every action the AI should take, who may take it, and what it must not do. Then we build one MCP server that any compliant client can use, with tools named and described so the model picks the right one. Remote servers sign people in through OAuth, so each call runs with that person's permissions. Write tools ask for confirmation, and every call is logged. Your AI gets real access, not a skeleton key.
Tool inventory before code
Every tool listed with its inputs, outputs, owner, and the data it can read or change.
Per-user access, not admin keys
Remote servers sign each person in, so tool calls run with their own permissions.
Every call on the record
Each tool call is logged with the user, inputs, result, and time for your security review.
Our MCP Server Development Process
From Tool Inventory to Live MCP Server in Five Clear Steps
You approve the tool list before we write code. Then you try each tool in your own AI client on staging and sign off on access rules before production.
We go through the systems you want AI to reach, the clients your team uses, and the actions that worry your security lead. You get a written scope, a tool shortlist, and a fixed price before any credentials are shared.
What's Included
What You Get With a Custom MCP Server
A working server your AI clients can use on day one, plus the access rules, tests, and documentation your security team will ask for before they approve it.
Compare Options
DeveloperLook vs Prebuilt Server vs In-House Team
Prebuilt servers and your own engineers both get AI connected to tools. Here is where each one fits, and where each tends to stop once security reviews the setup.
| Feature | DeveloperLookBest fit | Prebuilt Server | In-House Team |
|---|---|---|---|
| Planning | Tool inventory approved first | Install and configure | Fits around sprint work |
| Your internal APIs | Built around your own systems | Popular SaaS tools only | Full access to your code |
| Access control | Per-user OAuth scopes | Varies by publisher | Depends on who builds it |
| Write actions | Confirmation rules per tool | Often all or nothing | Decided case by case |
| Setup speed | 1 to 6 weeks per server | Fastest, often same day | Competes with roadmap work |
| Testing | Inspector and client tests per tool | Publisher's own tests | Your existing test suite |
| Cost | Fixed price per scope | Free or low cost | Salary time, no invoice |
| Ownership | Code and docs you keep | Publisher controls updates | Kept by your team |
DeveloperLook
Prebuilt Server
- Planning
- DeveloperLook: Tool inventory approved firstPrebuilt Server: Install and configure
- Your internal APIs
- DeveloperLook: Built around your own systemsPrebuilt Server: Popular SaaS tools only
- Access control
- DeveloperLook: Per-user OAuth scopesPrebuilt Server: Varies by publisher
- Write actions
- DeveloperLook: Confirmation rules per toolPrebuilt Server: Often all or nothing
- Setup speed
- DeveloperLook: 1 to 6 weeks per serverPrebuilt Server: Fastest, often same day
- Testing
- DeveloperLook: Inspector and client tests per toolPrebuilt Server: Publisher's own tests
- Cost
- DeveloperLook: Fixed price per scopePrebuilt Server: Free or low cost
- Ownership
- DeveloperLook: Code and docs you keepPrebuilt Server: Publisher controls updates
Projects We've Built
SaaS Platforms and Dashboards We Have Built
Software we designed and built with APIs, dashboards, and user roles, the kind of system an MCP server exposes. They show our build work, not past MCP projects.
Saasmo Recruiting Software Web Design & Development
- Technology
- HR & Staffing
- SaaS Platform
Oasic Finance SaaS Web Design & Development
- Finance
- SaaS Platform
- Dashboard
Staffic Time Tracking and Employee Monitoring Software
- HR & Staffing
- SaaS Platform
- Dashboard
Logify Project Risk and Control Management Web App
- Logistics & Transportation
- Construction
- Dashboard
What Our Clients Say
What Clients Say After Launch
Feedback from teams we have built apps, platforms, and websites for.
We had a pretty specific idea of what we wanted our website to become, but I wasn’t sure how easily we could explain all of it to a development team. There were quite a few moving parts, and we were working toward a deadline, so I expected a few things to get lost along the way. That never really happened. They understood what we were trying to achieve, kept us updated throughout the process, and got everything ready within the agreed timeline. The final site feels much easier to navigate, works beautifully on mobile, and is noticeably smoother for our customers. It was a huge relief seeing the original idea actually turn into the website we had in mind.
The mobile experience is probably my favourite part of the new site. We had spent a lot of time making sure the desktop version looked right, and the team kept pushing us to think about how everything would actually feel on a phone too. They were right. The navigation is clean, the interactions feel natural, and everything we’ve tested so far works exactly as it should. They also went through the links, forms, integrations, and other details more than once before launch, which I really appreciated. And even after going live, we could still reach out when something came up. The whole process felt friendly, organised, and surprisingly straightforward.
What I appreciate most is that the website is actually easy for us to manage now. We can update content ourselves instead of sending a developer a message every time we need to change something small. They also paid attention to the technical SEO side, which mattered to us because we didn’t want a new website to create problems for the traffic we already had. I’ve probably asked more questions than I should have since launch, but every question has been handled patiently and without any fuss. The team is easy to work with, they explain things clearly, and when we need something changed, they focus on getting it done rather than making the process complicated.
1,250+
Projects shipped
10+
Years of experience
Top 3%
Upwork Top Rated Plus
5 stars
Average review score
1,250+
Projects shipped
10+
Years of experience
Top 3%
Upwork Top Rated Plus
5 stars
Average review score
Want results like these?
Tell us what you are building. You get a reply within 24 hours on business days, then a written estimate.
Work Culture
Why Product Teams Trust Us With MCP Development
Model Context Protocol security starts at the server, which sits between an AI model and your business systems. So we build it as an access layer first and an integration second.
Least privilege by default
Each tool gets the narrowest scope that does its job, and read tools stay apart from write tools. A confused model can only do what that one tool allows.
Tool descriptions get tested
The model knows a tool only by its name and description, so vague wording turns into wrong actions. We rewrite both until your real requests pick the right tool.
Spec first, client neutral
We follow the current MCP specification rather than one client's quirks. Your server keeps working when your team moves from one AI tool to another.
Honest about prebuilt servers
Many SaaS vendors already publish an official MCP server. If one covers your case, we'll tell you before we quote and help you configure it instead.
You own the server
The repository, deployment, and credentials sit in your accounts from day one. There is no licence fee and no lock-in to our hosting.
Security team in the loop
We share the tool inventory and access rules with your security lead before the build. Their questions get answered early instead of blocking launch.
Overview
Who builds a model context protocol server for company tools and data?
DeveloperLook builds Model Context Protocol servers that let Claude, ChatGPT, Cursor, and your own agents use company systems safely. Each build includes a tool inventory, OAuth sign-in with per-user scopes, confirmations on write actions, call logs, and tests in your AI clients. Plans start at $1,150 and take 1 to 6 weeks, and you own the code.
DEVELOPERLOOK
Project facts · MCP Server Development
- Service
- MCP server development (tool inventory, server build, OAuth access, client tests)
- Typical timeline
- 1 to 2 weeks for one internal API, 3 to 6 weeks for multi-system servers
- Platforms
- Claude, ChatGPT, Cursor, VS Code, and custom agents
- Built with
- Official MCP SDKs in TypeScript and Python, OAuth, Docker, MCP Inspector
- Experience
- 10+ years of development experience and 1,250+ shipped projects
- Post-launch support
- 30 days of fixes and questions after launch
- Communication
- Calls in US, UK, and AU business hours
- Starting price
- From $1,150 USD, fixed scope after a free scoping call
Reviewed 28 Oct 2026 by Saimul Islam
Good fit
SaaS companies adding AI access
Your customers ask whether they can use your product from Claude or ChatGPT, and you want one official server instead of a dozen community forks.
Internal platform and IT teams
Your staff use AI clients every day, and you need them to reach the CRM, wiki, and ticket system without anyone sharing admin API keys.
Engineering teams on AI coding tools
Your developers want Cursor, VS Code, or Claude Code to read internal docs, deploy logs, and feature flags through one approved server.
Teams running their own AI agents
You run custom agents and need tools with typed inputs, clear errors, and rate limits that hold when an agent gets stuck in a loop.
Agencies with AI-curious clients
Your client wants their assistant connected to an internal system, and you need a white-label build with documentation your team can support.
Not a fit
Budget under $1,150
You need a server for one internal API, but your budget is below $1,150. A prebuilt server from the tool's vendor, configured by your own team, is the better fit.
Chatbot for site visitors
You want a chatbot that answers customers on your website. Our AI Chatbot Development service builds that, with or without MCP underneath.
Answers from documents
You mainly need AI to answer questions from manuals, policies, and PDFs. Our RAG Development Services page covers search and answers over your documents.
No AI use case yet
You haven't decided which AI tasks are worth building. Start with AI Consulting Services, then build the server once the use case is clear.
Not sure we're the right fit?
Book a free call or message us. We'll tell you honestly whether we're the right team.
Pricing
Transparent Starting Prices for MCP Server Development
These are starting points in USD. Your final price depends on how many tools you expose and which systems they touch, and you get a fixed proposal after a free scoping call.
For Agencies
White-Label MCP Servers for Agencies and Studios
You own the client relationship. We supply the MCP engineering behind it: tool design, secure access, testing in the client's AI tools, and documentation your team hands over under your brand. Your client sees one delivery partner, and that partner is you.
How the partnership works
White label: your client only ever sees your brand
Your clientSees only your brand
You, the agencyOwn the relationship
DeveloperLookBehind your brand
Share the brief
Send the systems, AI clients, and actions your client wants, under NDA if needed.
- Client needs
- You share the details
- Details received
We scope and price
You get a tool inventory, a timeline, and a fixed price you can mark up.
- Scope and plan
- Proposal to resell
- Your quoteYOUR LOGO
Build and hand off
We build, test, and document the server, then hand it to your team.
- Done by our team
- Your brand on it
- Handed to your clientDone
- Your client
- You, the agency
- DeveloperLook
Share the brief
Send the systems, AI clients, and actions your client wants, under NDA if needed.
- Client needs
- You share the details
- Details received
We scope and price
You get a tool inventory, a timeline, and a fixed price you can mark up.
- Scope and plan
- Proposal to resell
- Your quoteYOUR LOGO
Build and hand off
We build, test, and document the server, then hand it to your team.
- Done by our team
- Your brand on it
- Handed to your clientDone
Partner with DeveloperLookDelivered under your brand
The Right Technology for Your Project
Technology Stack
Official MCP SDKs, standard sign-in, and the AI clients your team already uses, so the server keeps working as models and tools change.
Claude
ChatGPT
Google Gemini- Cursor
- VS Code
- Custom agents
TypeScript
Python
Node.js- MCP Inspector
Docker
- OAuth sign-in
- Okta
- Auth0
Cloudflare- OpenTelemetry
PostgreSQL
Salesforce
HubSpot
Slack- Jira
- GitHub
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Related Services
Services teams add when AI needs more than tool access: answers from documents, agents that act on their own, security reviews, and API work.
RAG Development Services
DeveloperLook provides RAG development services for teams whose AI must answer from their own documents. We build retrieval augmented generation pipelines with source syncing, permission-aware search, cited answers, and a scored test set, then hand over the code.
AI Agent Development Services
DeveloperLook provides AI agent development services for business workflows: agents that qualify leads, read documents, and update your systems, with guardrails, human approval, and tests on your real cases. Custom AI agent development on a fixed scope.
Custom GPT Development
DeveloperLook handles custom GPT development for teams moving off custom GPTs. We migrate each GPT to a ChatGPT plugin, rebuild custom actions as MCP server tools, or rebuild it as an API assistant on your website, tested on your real prompts.This page
MCP Server Development
Pairs with the 6 services around it
AI Security Assessment
DeveloperLook runs an AI security assessment for teams shipping chatbots, RAG assistants, and AI agents. We trace every path data takes through the model, check it against known LLM security risks, and hand over ranked findings with code-level fixes.
Agentic AI Development Services
DeveloperLook provides agentic AI development services for operations and product teams. We map a multi-step process as states, build an orchestrator with specialist agents, scope their tools, and add approval queues, spend limits, and scenario tests.
Claude and Gemini API Integration
DeveloperLook provides Claude and Gemini API integration for teams that want Anthropic and Google models inside Shopify, HubSpot, Zoho CRM, WhatsApp, Google Sheets, or n8n. One server holds your keys, runs the same tests on both models, and logs every request's cost.FAQ
Frequently Asked Questions
Straight answers on what MCP is, how it works, which AI clients support it, cost, and timelines.
Saimul IslamFounder & CEO, DeveloperLookStill have questions?
Send a message or book a call with the team.
Our MCP builds start at $1,150, and enterprise implementations across several systems with single sign-on start at $6,800. A hosted team server with write actions and logs starts at $3,200. The price moves with the number of systems, the write actions that need confirmation rules, and your sign-in setup. You get a fixed quote after the free scoping call.
A server for one internal API takes 1 to 2 weeks, and a remote server across several systems takes 3 to 6 weeks. Most of that time goes into the tool inventory, access rules, and testing. An SDK can build MCP server code for a demo in an afternoon, but a demo doesn't decide who may delete a record.
MCP is an open standard that lets AI applications connect to outside tools and data in one consistent way. An AI app such as Claude or ChatGPT is the host, and each connection runs through an MCP client to an MCP server. The server offers tools the model can call, resources it can read, and prompt templates people can pick.
MCP works by passing JSON-RPC 2.0 messages between a client inside the AI app and a server you control. The client asks what the server offers, the model picks a tool, and the host asks the user before running it. The server then calls your API and returns the result. Local servers run on the user's machine, and remote servers run over HTTP with OAuth sign-in.
MCP servers are small programs that expose one system, such as a CRM or a database, to AI applications as tools, resources, and prompts. Each server decides what the AI can see and do inside that system. A good one keeps read and write tools apart, checks every input, and logs each call, which is where most of our build time goes.
MCP is used to let AI assistants and agents work with live business systems instead of pasted text. Common Model Context Protocol use cases include support agents checking orders and sales teams querying the CRM from Claude. Developers read logs from Cursor, and finance staff pull invoice data into ChatGPT. Each case becomes one server, or one tool on a shared server.
The purpose of MCP is to replace one-off AI integrations with a single standard that any compliant app can use. Before it, each AI product needed its own plugin for each system, so integrations multiplied. Now you build one server for a system, and every compliant client reaches it under the same access rules.
Yes. ChatGPT connects to remote MCP servers through developer mode, and OpenAI's documentation lists support for both read and write tools. Write actions ask the user to confirm by default, while tools marked read-only don't. Plan access has changed during the rollout, so we check your ChatGPT workspace settings before we promise a connection.
Yes. The specification, the official SDKs, and reference servers are published openly on GitHub under the modelcontextprotocol organization. Anthropic introduced MCP and, in December 2025, donated it to the Agentic AI Foundation, a fund hosted by the Linux Foundation. Anyone can build clients and servers without paying a licence fee.
In software development, an MCP server is the adapter between an AI model and a system you own, built like any small backend service. It has code, tests, deployment, and versions. Teams often start from SDK commands that create MCP server scaffolding, then add tools one at a time. We hand over the repository and a guide, so your developers keep adding tools after launch.
Saimul IslamFounder & CEO, DeveloperLookStill have questions?
Send a message or book a call with the team.
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