Custom API MCP Beats SaaS Tool Switching
Build an MCP server from your own API in 30-60 minutes. Eliminates tab-switching tax across 5-10 SaaS tools.
Building an MCP server from your own API endpoints is faster than switching between SaaS dashboards and gives your LLM direct access to your data. A custom MCP server takes 30-60 minutes to build and eliminates the tab-switching tax that fragments every workflow.
The SaaS tool-switching problem
A typical developer workflow touches 5-10 SaaS tools per task: project management, analytics, CRM, docs, monitoring, and search. Each context switch costs 15-30 seconds and breaks concentration. Over a day, that adds up to 30-60 minutes of pure switching overhead.
Why MCP changes this
MCP (Model Context Protocol) lets your LLM call tools directly. Instead of opening Grafana to check metrics, you ask the LLM. Instead of switching to your CRM to look up a contact, the LLM queries it via MCP. The interface becomes conversational instead of navigational.
Building an MCP server from your API
If your internal tool has a REST API, wrapping it as an MCP server is straightforward. Here is a minimal example using the MCP SDK:
from mcp.server import Server
from mcp.types import Tool, TextContent
import requests, os
app = Server("internal-crm")
@app.tool()
async def search_contacts(query: str) -> list[TextContent]:
"""Search CRM contacts by name or company."""
resp = requests.get(
f"{os.environ['CRM_API_URL']}/contacts/search",
headers={"Authorization": f"Bearer {os.environ['CRM_TOKEN']}"},
params={"q": query},
)
contacts = resp.json().get("results", [])
lines = []
for c in contacts[:10]:
lines.append(f"{c['name']} - {c['company']} - {c['email']}")
return [TextContent(type="text", text="\n".join(lines))]
@app.tool()
async def get_deal_pipeline() -> list[TextContent]:
"""Get current deal pipeline summary."""
resp = requests.get(
f"{os.environ['CRM_API_URL']}/deals/pipeline",
headers={"Authorization": f"Bearer {os.environ['CRM_TOKEN']}"},
)
deals = resp.json().get("stages", [])
summary = []
for stage in deals:
summary.append(f"{stage['name']}: {stage['count']} deals, ${stage['value']:,.0f}")
return [TextContent(type="text", text="\n".join(summary))]The pattern: Swagger to MCP
If your API has an OpenAPI (Swagger) spec, you can auto-generate MCP tool definitions from it. Each endpoint becomes a tool. Path parameters become tool arguments. Response schemas become tool output types.
import yaml
def openapi_to_mcp_tools(spec_path):
with open(spec_path) as f:
spec = yaml.safe_load(f)
tools = []
for path, methods in spec.get("paths", {}).items():
for method, details in methods.items():
if method not in ("get", "post"):
continue
params = details.get("parameters", [])
tool = {
"name": details.get("operationId", path.replace("/", "_")),
"description": details.get("summary", ""),
"parameters": [
{"name": p["name"], "type": p["schema"]["type"]}
for p in params if p["in"] in ("query", "path")
],
}
tools.append(tool)
return toolsReal examples from Reddit
Developers on Reddit report building custom MCP servers for:
- Internal analytics dashboards (replace Grafana tab-switching)
- Inventory management systems (query stock levels conversationally)
- Customer support ticket systems (search and update tickets via LLM)
- Deployment pipelines (trigger deploys and check status)
- Search APIs (add web search grounding to any LLM)
Adding search to your custom MCP stack
Pair your internal MCP server with a search MCP for external data. Your LLM can then query both internal systems and the open web in a single conversation.
{
"mcpServers": {
"internal-crm": {
"command": "python",
"args": ["mcp_crm_server.py"]
},
"scavio-search": {
"url": "https://mcp.scavio.dev/mcp",
"headers": {
"Authorization": "Bearer YOUR_API_KEY"
}
}
}
}Bottom line
Every REST API you use daily is a candidate for an MCP server. The build time is 30-60 minutes. The payoff is eliminating context switches for the lifetime of the tool. Start with your most-used internal API and expand from there.