MCP Server vs REST API: Choosing the Right Pounce Integration
Pounce offers two ways to access B2B company intelligence: a REST API and an MCP (Model Context Protocol) Server. Both give you access to the same underlying data — 65M+ companies from 20 countries — but they serve different use cases.
What Is MCP?
The Model Context Protocol (MCP) is a standard for connecting AI models to external data sources. Instead of writing API calls manually, your AI agent discovers and uses Pounce tools naturally through the MCP interface.
Think of it this way:
- REST API = You write the integration code
- MCP Server = Your AI agent writes the integration code
REST API: When to Use It
The REST API is the right choice when:
1. Building Data Pipelines
You have a predictable, repeatable workflow: search → enrich → store. The REST API gives you full control over request parameters, error handling, and retry logic.
import requests
headers = {"X-API-Key": "YOUR_KEY"}
# Deterministic pipeline: search, filter, enrich
results = requests.get(
"https://api.pounce.ch/api/v1/v2/entities/search",
headers=headers,
params={"q": "fintech", "country": "CH", "limit": 50}
).json()
for company in results["items"]:
enriched = requests.get(
f"https://api.pounce.ch/api/v1/v2/entities/{company['id']}",
headers=headers
).json()
save_to_database(enriched)2. Backend Services
Your server-side application needs company data as part of a larger workflow. REST is familiar, well-documented, and easy to integrate with any language or framework.
3. High-Volume Batch Operations
When processing thousands of companies, you want fine-grained control over concurrency, rate limiting, and error handling. REST gives you that control.
MCP Server: When to Use It
The MCP Server is the right choice when:
1. Building AI Agents
Your agent needs to discover and research companies as part of a broader task. With MCP, the agent can search and look up company details without hardcoded API calls.
User: "Find cybersecurity companies in Zurich with more than 50 employees"
Agent (via MCP):
1. Calls pounce_search(q="cybersecurity", country="CH", city="Zurich")
2. For each result, calls pounce_enrich(id=...)
3. Filters for employee_estimate > 50
4. Returns formatted results2. Natural Language Queries
Users describe what they want in plain language, and your AI agent translates that into the right API calls. MCP makes the agent's tool usage seamless.
3. Multi-Step Research Tasks
Complex research tasks where the agent needs to iterate: search → inspect results → refine search → enrich → compare. MCP lets the agent drive the research flow.
Feature Comparison
| Feature | REST API | MCP Server |
|---|---|---|
| Search companies | ✅ | ✅ |
| Enrich profiles | ✅ | ✅ |
| Webhooks | ✅ (registration) | ❌ |
| Batch operations | ✅ | ❌ |
| AI agent integration | Manual | Native |
| Rate control | Full control | Automatic |
| Language support | Any HTTP client | MCP-compatible agents |
Setting Up the MCP Server
Add Pounce to your MCP-compatible agent's configuration:
{
"mcpServers": {
"pounce": {
"command": "npx",
"args": ["-y", "@pounce/mcp-server"],
"env": {
"POUNCE_API_KEY": "your_api_key"
}
}
}
}Your agent can now use Pounce tools directly:
pounce_search— Search companies by any criteriapounce_enrich— Get full company profilespounce_stats— Get database statistics
Our Recommendation
- Start with REST if you are building a deterministic data pipeline or backend service
- Start with MCP if you are building an AI agent that needs to research companies
- Use both if your application has both automated pipelines and AI-driven research
Both options are available on all plans, including the free tier with 100 API calls per month.
Get started at pounce.ch. Full documentation at pounce.ch/dashboard/docs.