Borrowing it
Nothing to install: this file belongs to francisco-perez-sorrosal/linkedin-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/francisco-perez-sorrosal/linkedin-mcp/main/CLAUDE.mdgit clone --depth 1 https://github.com/francisco-perez-sorrosal/linkedin-mcpWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/instructions/francisco-perez-sorrosal/linkedin-mcp/claude-md)<a href="https://agentmods.dev/instructions/francisco-perez-sorrosal/linkedin-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/francisco-perez-sorrosal/linkedin-mcp/claude-md/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/instructions/francisco-perez-sorrosal/linkedin-mcp/claude-md"><img src="https://agentmods.dev/badge/instructions/francisco-perez-sorrosal/linkedin-mcp/claude-md.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.02619 | $0.02619 |
| Opus 5.5 | $0.01048 | $0.01048 |
| Sonnet 5.5 | $0.00524 | $0.00524 |
| Haiku 4.5 | $0.00262 | $0.00262 |
Grade A, and why
linkedin-mcp CLAUDE.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
CLAUDE.md
This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
Development Commands
This project uses Pixi for dependency management and task execution. Key commands:
# Install dependencies
pixi install
# Run MCP server
pixi run mcps --transport stdio # For local development
pixi run mcps --transport streamable-http # For remote/HTTP access
# Development tasks
pixi run test # Run tests (117 tests, pytest configured)
pixi run lint # Check linting
pixi run format # Apply formatting and fix lint issues
pixi run build # Build package (creates sdist/wheel in dist/)
Alternative execution methods:
# Direct execution with uv
uv run --with "mcp[cli]" mcp run src/linkedin_mcp_server/main.py --transport streamable-http
# MCP inspection mode
DANGEROUSLY_OMIT_AUTH=true npx @modelcontextprotocol/inspector pixi run mcps --transport stdio
Architecture Overview
This is a LinkedIn MCP Server with autonomous background scraping that exposes LinkedIn job data via the Model Context Protocol (MCP). The system has four main architectural components:
1. MCP Server (main.py)
- Built with FastMCP framework
- Configurable transport modes: stdio, streamable-http
- Exposes 11 tools for job querying, profile management, application tracking, and analytics
- Cache-first serving: queries return instantly from SQLite database
- Auto-detects transport mode from environment variables (TRANSPORT, HOST, PORT)
2. Database Layer (db.py)
- SQLite with WAL mode for concurrent reads/writes
- FTS5 full-text search on job descriptions and titles
- 5 tables: jobs, scraping_profiles, applications, company_enrichment, job_changes
- Default location:
~/.linkedin-mcp/jobs.db - Composable queries with multiple filters
- Performance: <100ms for typical queries
3. Background Scraper Service (background_scraper.py)
- Runs continuously in MCP server process (async tasks)
- One worker per scraping profile (configurable via MCP tools)
- Default profile: San Francisco, CA, 25mi, "AI Engineer or ML Engineer or Principal Research Engineer", 2h refresh
- Semaphore(10) for job detail scraping, Semaphore(2) for company enrichment
- Adaptive rate limiting with exponential backoff on 429/503 errors
- Graceful startup/shutdown with asyncio task coordination
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2mo ago First seen · 268 lines · 2,619 tokens per session scan A 2aee75f36ace
linkedin-mcp CLAUDE.md is an instructions file published in the GitHub repository francisco-perez-sorrosal/linkedin-mcp (2 stars, last pushed 8mo ago), licensed MIT. It adds 2,619 tokens to every session, about $0.0105 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
linkedin-mcp CLAUDE.md compared with instructions from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| linkedin-mcp CLAUDE.mdfrancisco-perez-sorrosal/linkedin-mcp | Instructions file | 2 | 8mo ago | MIT | A | 2,619 | Claude Code |
| vscode buildNext.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 9,719 | GitHub Copilot |
| next.js AGENTS.mdvercel/next.js | Instructions file | 143k | yesterday | MIT | A | 7,461 | Codex, OpenCode |
| langchain AGENTS.mdlangchain-ai/langchain | Instructions file | 149k | yesterday | MIT | A | 4,680 | Codex, OpenCode |
| vscode oss-third-party-notices.instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 5,516 | GitHub Copilot |
| vscode copilot-instructions.mdmicrosoft/vscode | Instructions file | 195k | yesterday | MIT | A | 3,093 | GitHub Copilot |
| deepseek-harness AGENTS.mddeepseek-ai/deepseek-harness | Instructions file | 274k | 2d ago | MIT | A | 4,313 | Codex, OpenCode |