remote-mcp-linkedin
# remote-mcp-linkedin
Read-only MCP server for collecting visible LinkedIn profile, post, and
contact-network data and turning profile data into structured dossiers. It uses
a local browser bridge, so the browser session stays on the user's machine.
## What it does
- Opens visible LinkedIn profile pages through a local browser bridge
- Extracts basic public / visible profile data
- Collects visible profile posts from `/recent-activity/all/`
- Searches visible people/contact-network results, defaulting to 1st-degree contacts
- Builds a structured dossier for AI agents
- Exposes the result through MCP tools
## MCP tools
- `linkedin_profile_get` - returns normalized visible profile data
- `linkedin_profile_posts` - returns visible posts from a profile activity page
- `linkedin_contact_network_search` - returns visible people-search/contact results
- `linkedin_profile_dossier` - returns a structured profile dossier with evidence, gaps, warnings, and confidence
## Safety
- Read-only by design
- No messages, likes, comments, connection requests, or job actions
- No cookies or browser session data are exported
- The browser runs locally
## Quickstart
```bash
python3 -m venv .venv
source .venv/bin/activate
python -m pip install -e ".[dev]"
export REMOTE_MCP_LINKEDIN_BRIDGE_TOKEN="replace-with-a-long-random-token"
remote-mcp-linkedin-server
remote-mcp-linkedin-bridge
```
## Status
Small prototype.
Working:
- MCP server
- local bridge
- profile data tool
- profile posts tool
- contact network search tool
- dossier tool
- JSON result storage
Not ready:
- robust LinkedIn parser
- production extraction
- advanced profile analysis
## License
MIT
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one produces a structured dossier, the other returns raw profile sections. There is no ambiguity about which to use for a given task.
Both tools share the 'linkedin_profile_' prefix, but one uses a noun ('dossier') and the other a verb ('get'), which is inconsistent. A consistent verb-noun pattern would be clearer.
With only two tools, the server feels thin for a platform as broad as LinkedIn. However, the tools are well-focused on profile data, so the count is borderline reasonable for that narrow scope.
The server covers only profile retrieval in two formats, missing many common LinkedIn operations like searching, messaging, or managing connections. This is a significant gap for general LinkedIn usage.