MCP Server

Here's the thing: every AI agent needs to interact with external systems. Your CRM, your database, your website, your analytics — agents need to read from and write to all of them. Without a standard protocol, you'd build a custom integration for every agent-platform combination. That's an N-times-M problem that doesn't scale.

MCP Servers solve this by being the universal adapter. Build one MCP Server for your CRM, and every MCP-compatible agent — Claude, GPT, Gemini, open-source agents — can use it. One integration, universal access.

For B2B companies, this changes the game. Your product becomes accessible to AI agents that are making purchasing decisions, doing research, and automating workflows for your prospects. If you don't have an MCP Server, agents can't interact with your product programmatically. You're a black box to the agentic ecosystem.

An MCP Server exposes three types of primitives:

1. Tools — executable functions the agent can call. Each tool has a name, a natural-language description, and a JSON Schema defining its parameters. Example: search_knowledge_base(query: string, limit: number).

2. Resources — read-only data the agent can access. Think of these as files, database records, or configuration values. They have URIs and MIME types.

3. Prompts — reusable prompt templates that help agents use the server's tools effectively. These are optional but powerful for complex workflows.

The lifecycle is simple. An agent connects to the server. It calls tools/list to discover available tools. It reads the descriptions and schemas. Then it calls tools/call with the appropriate parameters. The server executes the logic and returns structured results.

All communication runs over JSON-RPC 2.0. For local integrations, the transport is stdio (stdin/stdout). For remote servers, it's HTTP with Server-Sent Events for streaming responses.

A SaaS company builds an MCP Server for their customer support knowledge base. The server exposes three tools: search_articles, get_article_by_id, and submit_ticket.

A customer's AI assistant connects to the MCP Server and calls search_articles("how to set up SSO with Okta"). The server queries their help center database and returns the top 3 matching articles with titles, summaries, and links. The agent reads the most relevant one, walks the customer through the setup, and if they hit a snag, calls submit_ticket with the error details.

The customer never opened a browser. The support team never fielded a ticket they didn't need to. The MCP Server turned their static knowledge base into an interactive resource that agents can query on behalf of customers 24/7.

• Writing poor tool descriptions. Agents select tools based on descriptions alone. "Does stuff" is useless. "Searches the product knowledge base by keyword and returns matching articles with relevance scores" gives the agent everything it needs to decide when to use it.
• Returning unstructured text. If your tool returns a blob of prose, the agent has to parse it. Return structured JSON with clear fields. Let the agent decide how to present it.
• No input validation. Agents will send unexpected inputs. Your server needs to validate parameters against the JSON Schema you published and return clear error messages — not crash.
• Ignoring the stdio transport. Remote HTTP servers are great for production, but local stdio servers are essential for development and testing. Support both from day one.
• Making tools too granular. If an agent needs to call 8 tools in sequence to complete one task, you've over-decomposed. Provide higher-level tools that combine common workflows.