Borrowing it
Nothing to install: this file belongs to expandingideas-ai/mcp-wet. 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/expandingideas-ai/mcp-wet/main/AGENTS.mdgit clone --depth 1 https://github.com/expandingideas-ai/mcp-wetWrote 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/expandingideas-ai/mcp-wet/agents-md)<a href="https://agentmods.dev/instructions/expandingideas-ai/mcp-wet/agents-md"><img src="https://agentmods.dev/badge/instructions/expandingideas-ai/mcp-wet/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/expandingideas-ai/mcp-wet/agents-md"><img src="https://agentmods.dev/badge/instructions/expandingideas-ai/mcp-wet/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 | $0.03162 | $0.03162 |
| Opus 5 | $0.01581 | $0.01581 |
| Sonnet 5 | $0.00632 | $0.00632 |
| Haiku 4.5 | $0.00316 | $0.00316 |
Grade A, and why
mcp-wet 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- mcp-wet CLAUDE.md — 100% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AGENTS.md - wet-mcp
Python MCP Server: web search, content extraction, library docs, structured extraction.
Xem AGENTS.md va README.md de hieu architecture va configuration.
Cau truc
src/wet_mcp/-- Package chinh (src layout)server.py-- FastMCP server (orchestrator, file lon nhat)config.py-- Pydantic Settings (singleton)cache.py,db.py,embedder.py,reranker.py-- Infrastructurerelay_setup.py--apply_config/load_config_from_fileenv-applier used by the OAuth setup form (live setup UX = OAuth-AS browser form at<PUBLIC_URL>/authorize; theensure_configcreate-session/poll path is legacy/unused in production)relay_schema.py-- Relay form schema (2 modes: local/cloud)sync/-- Docs sync backends:gdrive.py(Google Drive, OAuth Device Code, httpx) +s3.py(S3/R2/B2 operator mode) +base.pytoken_store.py-- Local token storage cho OAuth (~/.wet-mcp/tokens/)setup_tool.py-- Warmup + setup-sync logic (MCP-callable)sources/-- Data source integrations (crawler, docs, searxng)
tests/-- Mirror source modules
Lenh thuong dung
uv sync --group dev # Cai dependencies
uv build # Build package (hatchling)
uv run ruff check . # Lint
uv run ruff format --check . # Kiem tra format
uv run ruff check --fix . && uv run ruff format . # Fix
uv run ty check # Type check (ty lenient config)
uv run pytest # Test tat ca (integration excluded by default)
uv run pytest -m integration # Chi integration tests
uv run pytest tests/test_config.py::test_function_name -v # Test don le
uv run wet-mcp # Chay server
# Mise shortcuts
mise run setup # Full dev env setup
mise run lint # ruff check + ruff format --check + ty check
mise run test # pytest
mise run fix # ruff check --fix --unsafe-fixes + ruff format
mise run dev # uv run wet-mcp
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 · 187 lines · 3,162 tokens per session scan A 0d0aec152891
mcp-wet AGENTS.md is an instructions file published in the GitHub repository expandingideas-ai/mcp-wet (0 stars, last pushed 2mo ago), licensed MIT. It adds 3,162 tokens to every session, about $0.0158 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 2026-09-01.
Other instructions, from other repositories
next.js AGENTS.md
AGENTS.md instructions for vercel/next.js, covering next.js development guide, codebase structure, monorepo overview, core package: packages/next and other important packages.
codex AGENTS.md
AGENTS.md instructions for openai/codex, covering rust/codex-rs, the codex-core crate, code review rules, crate api surface and model visible context.
vscode buildNext.instructions.md
Working notes and architecture documentation for the new esbuild-based build system in build/next. Use when making changes to the new build pipeline (transpile/bundle commands, NLS plugin, source-map handling, resource copying, or self-hosting watch tasks).
vscode oss-third-party-notices.instructions.md
Instructions for microsoft/vscode, covering vs code oss third-party-notices pipeline, architecture, pipeline flow in ci, applying the notice (cutover) and fallback chain (never fail the build).
langchain AGENTS.md
AGENTS.md instructions for langchain-ai/langchain, covering global development guidelines for the langchain monorepo, corridor security analysis, project architecture and context, monorepo structure and development tools & commands.
deepseek-harness AGENTS.md
AGENTS.md instructions for deepseek-ai/deepseek-harness, covering agents.md, pre-stable apis and released session data, repository layout, commands and host sandbox failures.