MCP Server Development Service — Built for You

✓ Python or TypeScript ✓ Local or remote (Streamable HTTP) ✓ OAuth & scoped permissions ✓ Tested with real MCP clients

Quick answer

What is an MCP server? 

An MCP server is a small service that exposes your tools (actions), resources (data) and prompts to AI applications through the Model Context Protocol, an open standard introduced by Anthropic in November 2024. Any MCP-compatible client, such as Claude, ChatGPT, IDE assistants or custom agents, can then use them.

What does this service include? 

Codersarts engineers build a custom MCP server for your systems. That covers tool design, authentication and permission scoping, testing with MCP Inspector and real clients, and optional remote deployment with OAuth.

How long and how much? 

Starter from $149 (3 days, up to 3 tools, local). Standard $299 (5 days, up to 10 tools with auth). Advanced $599 (10 days, remote server with OAuth, deployment and logging).

Is this your problem?

• Your AI assistant can't see your internal tools, database or documents

• Your team copies data into ChatGPT or Claude by hand

• You built a quick MCP server, but it only works on one laptop

• The model calls the wrong tool or passes bad arguments

• Security won't approve AI access to company systems without auth and permission controls

• You need one integration that works across Claude, ChatGPT and your own agents

Why MCP matters now

• It's becoming the industry standard. Anthropic introduced MCP in November 2024. OpenAI adopted it in March 2025, followed by Google DeepMind, and Microsoft integrated it into its AI tooling. In December 2025 Anthropic donated MCP to the Agentic AI Foundation, a Linux Foundation fund co-founded by Anthropic, Block and OpenAI.

• Build once, use everywhere. MCP replaces one-off connectors for each AI app. One server can serve many MCP clients.

• Agents need tools. Agents are only as useful as the systems they can act on. MCP is the standard way to give them safe access.

Why DIY MCP servers fail in production

• Vague tool definitions. Unclear names, descriptions and schemas make the model pick the wrong tool or send bad inputs.

• Local-only setup. A stdio server on one machine can't be shared with a team or hosted service. Remote servers need Streamable HTTP and proper hosting.

• Weak authorization. The MCP authorization spec requires OAuth 2.1 with PKCE for remote servers. Common mistakes include poor token validation, token passthrough and overly broad scopes.

• Security risks. Researchers have documented prompt injection and tool poisoning, where malicious instructions hidden in tool descriptions or data can lead to data leaks.

• No testing. Servers are rarely tested against real clients, edge cases and permission limits before people rely on them.

We design tools for the model, lock down access, and test with real clients before handover.

How an MCP server works (simple view)

AI app (Claude, ChatGPT, IDE, your agent)  ←→  MCP client
                    ↓  JSON-RPC over stdio or Streamable HTTP
              Your MCP server
   ├── Tools      → actions (create ticket, query orders, send email)
   ├── Resources  → data (docs, records, files)
   └── Prompts    → reusable instructions/templates
                    ↓  scoped, authenticated calls
   Your systems: APIs · databases · SaaS apps · internal services

What we do

1. Scope check: choose which tools, data and actions to expose, and who may use them.

2. Tool design: write clear names, descriptions and input/output schemas that models use correctly.

3. Build: implement the server in Python (official SDK / FastMCP) or TypeScript (official SDK).

4. Secure: add read-only defaults, scoped permissions, input validation, secret handling, and OAuth 2.1 for remote servers.

5. Test: check every tool with MCP Inspector, then in your target clients (Claude Desktop, IDEs or your agent).

6. Deploy (Advanced): host it as a remote server over Streamable HTTP on your cloud, with logging and monitoring.

7. Document & hand over: setup guide, tool reference and client connection instructions.

Packages

Add-ons: Priority delivery (+50%) · Extra tool (+$29) · Remote deployment (+$79) · OAuth setup (+$79) · 30-min walkthrough call (+$25) · 14-day support (+$39)

Final scope and price are confirmed after a free 30-minute scope check. You pay only after you approve the quote.

What we can connect

Clients we test with: Claude Desktop, Claude Code, ChatGPT (MCP connectors), Cursor, VS Code, and custom agents (LangGraph, OpenAI Agents SDK, Claude Agent SDK).

Use cases

• Support agent: look up customers, orders and tickets, then draft replies

• Sales assistant: query CRM deals, log notes and create follow-ups

• Data assistant: run approved, read-only database queries in plain English

• Engineering assistant: search internal docs, open issues and check deployments

• Operations: trigger approved workflows such as refunds, reports or provisioning, with human approval

Local vs remote MCP server

Security we build in

• Least privilege: read-only by default, with write actions only where you approve them

• Scoped permissions per tool and per user role, with no broad "super" scopes

• OAuth 2.1 with PKCE for remote servers, plus token validation for issuer, audience and expiry

• No token passthrough: the server accepts only tokens issued for it

• Input validation and output limits on every tool

• Tool descriptions we write and review, to reduce prompt-injection and tool-poisoning risk

• Human approval for high-risk actions, on request

• Audit logs of tool calls (Advanced)

What you get

• A working MCP server, in Python or TypeScript

• Tool, resource and prompt definitions written for model accuracy

• Test results from MCP Inspector and your target clients

• Auth and permission setup

• Deployment and monitoring (Advanced)

• Setup guide, tool reference and client connection steps

• Proof of done: a recorded or live demo of your AI client using the tools

What we need from you

Required (the delivery clock starts once we have these):

• The tools, APIs or data to expose, and what each should do

• API docs plus test credentials or a sandbox

• Target MCP clients (Claude Desktop, ChatGPT, IDE or custom agent)

• Who should have access, and with what permissions

• What a successful result looks like (definition of done)

Optional:

• Preferred language (Python or TypeScript) and hosting

• Your identity provider for OAuth (Auth0, Okta, Azure AD, Google)

• Whether you need an NDA

How it works

1. Free scope check (30 min): confirm tools, access and package.

2. Build & secure: engineers design the tools, implement them and add auth.

3. Test & prove: you see your AI client calling your tools correctly.

4. Review & revise: adjust within your package's revisions, then hand over.

Why Codersarts Tasks

• One accountable team: a managed Codersarts engineering team owns delivery, not an individual freelancer.

• Model-first tool design: tools are written so models choose and call them correctly.

• Security by default: least privilege, scoped access and OAuth from the start.

• Room to grow: extend to agents, multi-agent systems or a full AI product.

Related tasks

• Deploy Remote MCP Server with OAuth: already have a local server?

• Build MCP Server for Your Database (Postgres, Supabase)

• Fix MCP Server Errors

• Build AI Agent Workflow

• Fix AI Agent Loops & Tool Calling

• Connect Agents with A2A Protocol

Bigger than one task? Plan an AI agent build →

FAQ

What is an MCP server? An MCP server exposes tools, data and prompts to AI applications using the Model Context Protocol, an open standard introduced by Anthropic in 2024. MCP-compatible clients such as Claude, ChatGPT, IDE assistants and custom agents can then use them.

How much does it cost to build an MCP server? From $149 for a local server with up to 3 tools. Standard ($299) covers up to 10 tools with auth and testing in your clients. Advanced ($599) is a deployed remote server with OAuth 2.1, logging and full documentation.

How long does it take? 3 days for Starter, 5 days for Standard and 10 days for Advanced, counted from when we receive your API docs and credentials.

Python or TypeScript: which should I choose? Both have official SDKs. We choose based on your existing stack and hosting: Python suits data and ML teams, and TypeScript suits web and Node.js teams.

Will it work with ChatGPT, or only Claude? MCP is an open standard supported across major AI platforms. We test your server in the clients you name.

What's the difference between a local and a remote MCP server? A local server runs on one user's machine over stdio. A remote server runs on your infrastructure over Streamable HTTP, so it can serve a team or hosted agents, and needs OAuth.

Is it safe to give AI access to our systems? It can be, when access is limited. We use read-only defaults, scoped permissions, OAuth 2.1 for remote servers, input validation, reviewed tool descriptions, optional human approval, and audit logs.

Can you connect our database? Yes. We build read-only database tools with query limits, or a dedicated database MCP server. See Build MCP Server for Your Database.

We already have an MCP server that isn't working. Can you fix it? Yes. Use Fix MCP Server Errors for debugging, or Deploy Remote MCP Server with OAuth to make a local server production-ready.

Do we own the code? Yes. You get the full source code and documentation, and we sign an NDA on request.

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