Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/windoc/easyocr-mcp/agents-mdgit clone --depth 1 https://github.com/WindoC/easyocr-mcpWhat 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 | $0.00371 | $0.00371 |
| Opus 5 | $0.00186 | $0.00186 |
| Sonnet 5 | $0.00074 | $0.00074 |
| Haiku 4.5 | $0.00037 | $0.00037 |
Grade A, and why
easyocr-mcp 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 2d 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.
What it actually says
AGENTS.md — easyocr-mcp
This repository contains an MCP (Model Context Protocol) server that provides OCR using EasyOCR. Details below mirror what exists in this repo and its README.
Scope
- Applies to this repository rooted here.
Overview
- Name: EasyOCR MCP Server
- Purpose: Provide OCR tools via EasyOCR for images (base64, file, URL).
- Entry point:
easyocr-mcp.py
Current Files (key)
easyocr-mcp.py— MCP server and OCR tools.README.md— Features, install, usage, configuration, examples.pyproject.toml— Project metadata and dependencies.test.py,test-gpu.py,test_mcp_tools.py— Local test scripts.test.png— Sample image.LICENSE,.gitignore,uv.lock,plan.md— Licensing, ignores, lockfile, notes.
Tools (as implemented)
ocr_image_base64— OCR from base64-encoded image.ocr_image_file— OCR from an image file path.ocr_image_url— OCR from an image URL.
Configuration
- Environment:
EASYOCR_LANGUAGES— comma-separated language codes (defaulten).
Install & Run (see README for details)
- Optional GPU (PyTorch): see README for the exact
uv pip install ...command. - Install deps and run:
uv syncuv run easyocr-mcp.py- Example tests:
uv run test.py,uv run test-gpu.py
MCP Client Config
- See README section “MCP Configuration Example” for Windows and Linux/macOS JSON snippets.
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.
- 2d ago First seen · 39 lines · 371 tokens per session scan A d11c4bd62dd8
easyocr-mcp AGENTS.md is an instructions file published in the GitHub repository WindoC/easyocr-mcp (2 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 371 tokens to every session, about $0.0019 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.
Other instructions, from other repositories
GPT-RAG release.instructions.md
Instructions for Azure/GPT-RAG, a project described as: Sharing the learning along the way we been gathering to enable Azure OpenAI at enterprise scale in a secure manner. GPT-RAG core is a Retrieval-Augmented Generation pattern running in Azure, using Azure Cognitive Search for retrieval and Azure OpenAI large…
OpenCandle AGENTS.md
Instructions for Kahtaf/OpenCandle, covering opencandle, commands, where to look, code style and conventions.
oci-agent CLAUDE.md
Instructions for Netflix-Skunkworks/oci-agent, covering observational causal inference (oci) agent and rules.
ZipAI CLAUDE.md
Claude Code instructions for nickdesi/ZipAI, covering claude.md — zipai: ultra-dense token optimizer, rules, 1. zero filler, 2. ambiguity and 3. prompt caching.
TreeSkill CLAUDE.md
Instructions for JimmyMa99/TreeSkill, covering claude.md, project overview, commands, install and run tests.
AutoRAG-Research CLAUDE.md
Instructions for NomaDamas/AutoRAG-Research, covering claude.md, project overview, common commands, setup and code quality.