DataForB2B MCP Server
Official# DataForB2B MCP Server
An [MCP](https://modelcontextprotocol.io) server for the [DataForB2B API](https://docs.dataforb2b.ai) — search and enrich companies and people (professional profiles), job postings, and social posts, directly from Cursor, Claude, or any other MCP-compatible client.
## Tools
| Tool | Description |
|---|---|
| `search_people` | Search professional profiles with structured filters (title, company, location, skills, funding stage, ...). |
| `search_company` | Search companies with structured filters (industry, size, country, funding, hiring, ...). |
| `search_jobs` | Search job postings on LinkedIn / Indeed — hiring signals. |
| `search_posts` | Search social posts on LinkedIn / Twitter / Reddit by keyword, or fetch a single post's engagers as leads. |
| `enrich_profile` | Enrich a single profile (full profile data, work email, personal email, GitHub). |
| `enrich_company` | Enrich a single company (description, headquarters, employee count, funding, offices). |
Full parameter reference: [docs.dataforb2b.ai/api-reference](https://docs.dataforb2b.ai/api-reference/search-people).
## Getting an API key
Sign up at [dataforb2b.ai](https://dataforb2b.ai) and grab your API key from the dashboard.
## Install
Requires Python 3.10+. The recommended way to run it is with [`uvx`](https://docs.astral.sh/uv/guides/tools/), which needs no separate install step and always runs the latest commit on `main`:
```bash
uvx --from git+https://github.com/DataForB2B/dataforb2b-mcp dataforb2b-mcp
```
Or clone and install it with pip:
```bash
git clone https://github.com/DataForB2B/dataforb2b-mcp.git
pip install ./dataforb2b-mcp
```
> Once published on PyPI, this collapses to plain `uvx dataforb2b-mcp` / `pip install dataforb2b-mcp` (the config examples below use the git form, which works right now).
## Configure
### Cursor
Add to `.cursor/mcp.json` (project) or `~/.cursor/mcp.json` (global):
```json
{
"mcpServers": {
"dataforb2b": {
"command": "uvx",
"args": ["--from", "git+https://github.com/DataForB2B/dataforb2b-mcp", "dataforb2b-mcp"],
"env": {
"DATAFORB2B_API_KEY": "your-api-key"
}
}
}
}
```
### Claude Desktop / Claude Code
Add to `claude_desktop_config.json` (Desktop) or run `claude mcp add` (Claude Code), using the same shape:
```json
{
"mcpServers": {
"dataforb2b": {
"command": "uvx",
"args": ["--from", "git+https://github.com/DataForB2B/dataforb2b-mcp", "dataforb2b-mcp"],
"env": {
"DATAFORB2B_API_KEY": "your-api-key"
}
}
}
}
```
### Any other MCP client
Same `command`/`args`/`env` shape as above — every MCP client that supports stdio servers reads it the same way. Alternatively, run the server yourself and point your client at the process:
```bash
DATAFORB2B_API_KEY=your-api-key uvx --from git+https://github.com/DataForB2B/dataforb2b-mcp dataforb2b-mcp
```
or with the flag instead of the env var:
```bash
uvx --from git+https://github.com/DataForB2B/dataforb2b-mcp dataforb2b-mcp --api-key your-api-key
```
## Development
```bash
git clone https://github.com/dataforb2b/dataforb2b-mcp.git
cd dataforb2b-mcp
uv sync
DATAFORB2B_API_KEY=your-api-key uv run dataforb2b-mcp
```
Inspect the server interactively with the [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector):
```bash
DATAFORB2B_API_KEY=your-api-key npx @modelcontextprotocol/inspector uv run dataforb2b-mcp
```
## License
MIT
TDQS
Scored across 6 tools
Each tool targets a distinct resource type (people, companies, posts, jobs) or a distinct action (search vs. enrich). The descriptions clearly differentiate search_people from enrich_profile and search_company from enrich_company, with no overlapping purposes.
All tool names follow a consistent verb_noun pattern: search_* for discovery and enrich_* for data augmentation. This predictable structure makes it easy to infer function from the name alone.
With 6 tools, the set is well-scoped for a B2B data platform. Each tool covers a distinct data operation (search people, companies, posts, jobs; enrich profiles, companies) without redundancy or bloat.
The tool surface covers the full expected lifecycle for a B2B data provider: discovery via search across all major entity types, and enrichment for deeper data on individuals and companies. No obvious dead ends or missing critical operations like retrieving by ID or exporting results.