Borrowing it
Nothing to install: this file belongs to Huzaifa-ali/mcp-server-linkedin. 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/Huzaifa-ali/mcp-server-linkedin/main/AGENTS.mdgit clone --depth 1 https://github.com/Huzaifa-ali/mcp-server-linkedinWrote 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/huzaifa-ali/mcp-server-linkedin/agents-md)<a href="https://agentmods.dev/instructions/huzaifa-ali/mcp-server-linkedin/agents-md"><img src="https://agentmods.dev/badge/instructions/huzaifa-ali/mcp-server-linkedin/agents-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/huzaifa-ali/mcp-server-linkedin/agents-md"><img src="https://agentmods.dev/badge/instructions/huzaifa-ali/mcp-server-linkedin/agents-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 | $0.01208 | $0.01208 |
| Opus 5 | $0.00604 | $0.00604 |
| Sonnet 5 | $0.00242 | $0.00242 |
| Haiku 4.5 | $0.00121 | $0.00121 |
Grade A, and why
mcp-server-linkedin AGENTS.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 8d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md — AI Development Guidelines
Audience: LLM-driven engineering agents
Repository Map
| Path | Purpose |
|---|---|
src/mcp_server_linkedin/ |
Library source (Python ≥ 3.10) |
├─ config.py |
Constants, API URLs, LinkedInSettings dataclass |
├─ exceptions.py |
Typed exception hierarchy (LinkedInAPIError, etc.) |
├─ server.py |
FastMCP instance + direct tool registration (entrypoint) |
├─ models/ |
Frozen dataclass models (LinkedInProfile, PostResult, etc.) |
├─ services/ |
Async API clients with connection pooling (LinkedInService) |
├─ tools/ |
MCP tool implementations (thin: validate → delegate → format) |
│ ├─ auth.py |
OAuth flow, profile, logout |
│ ├─ posting.py |
Text, image, video, article posts + delete |
│ └─ analytics.py |
Stubbed — pending Community Management API |
└─ utils/ |
Shared utilities (token persistence) |
tests/ |
Pytest suite (unit + integration) |
Architecture
- Single Responsibility: Each module does one thing. Config has no I/O. Token module has no API knowledge. Service has no MCP awareness.
- Dependency flow:
tools/ → services/ → models/+tools/ → utils/+tools/ → config.py - No wrapper pattern: Tools are registered directly with
mcp.tool()— no pass-through functions. - Connection pooling:
LinkedInServicewraps a persistenthttpx.AsyncClientwith proper lifecycle. - Typed models: API responses are parsed into frozen dataclasses with
from_api_response()factory methods. - Structured exceptions: Typed exception hierarchy caught at tool boundary, never leaks to MCP layer.
- Tool naming:
linkedin_{action}_{target}(e.g.,linkedin_post_text,linkedin_get_profile).
Key Principles
- Never crash the server. Every tool catches exceptions and returns a descriptive error string.
- Type hints everywhere. All functions, parameters, and return values.
- Google-style docstrings. On every public function.
- Constants over magic strings. All API URLs, headers, and values live in
config.py. - Tools return strings. Success messages include relevant IDs; errors include context.
- Single responsibility per file. If a module does two things, split it.
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.
- 8d ago First seen · 128 lines · 1,208 tokens per session scan A a3f8c629636a
mcp-server-linkedin AGENTS.md is an instructions file published in the GitHub repository Huzaifa-ali/mcp-server-linkedin (0 stars, last pushed 1mo ago), licensed MIT. It adds 1,208 tokens to every session, about $0.0060 per session on Opus 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.
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