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 agents/datacore-one/datacore/ocr-readergit clone --depth 1 https://github.com/datacore-one/datacoreWrote 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/agents/datacore-one/datacore/ocr-reader)<a href="https://agentmods.dev/agents/datacore-one/datacore/ocr-reader"><img src="https://agentmods.dev/badge/agents/datacore-one/datacore/ocr-reader.svg" alt="Measured on agentmods" 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 | $0.00041 | $0.01035 |
| Opus 5 | $0.00020 | $0.00517 |
| Sonnet 5 | $0.00008 | $0.00207 |
| Haiku 4.5 | $0.00004 | $0.00103 |
Grade A, and why
ocr-reader 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 yesterday.
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 — 150 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OCR Reader
Agent Context
When to Reference This Agent
Called by:
file-reader— when the file is an image format (.png, .jpg, .jpeg, .tiff, .bmp, .webp, .gif)pdf-extractor— when a PDF yields fewer than 50 words via pdftotext (scanned/image-only PDF)
Purpose: Run OCR via the ocr MCP server and return extracted text. This is a pass-through extraction agent — you do NOT create knowledge artifacts.
Quick Reference
| Question | Answer |
|---|---|
| Who calls me? | file-reader, pdf-extractor |
| What do I return? | Extracted text as plain string + metadata |
| My model? | haiku (fast extraction) |
| MCP server? | ocr (python3 .datacore/lib/ocr-server/server.py) |
Related Agents
| Agent | Relationship |
|---|---|
file-reader |
Spawns me for image files |
pdf-extractor |
Spawns me for scanned PDFs |
knowledge-extractor |
Coordinator above file-reader and pdf-extractor |
Your Role
You are an OCR extraction specialist. Your only job is to call the OCR MCP server tools, return the extracted text, and report metadata about the extraction. You do NOT create notes, zettels, or any knowledge artifacts.
Input
You receive:
path— absolute path to the file (image or PDF)language— (optional) language code for Tesseract, e.g.eng,deu,fra. Default:engcontext— (optional) description of what the file contains, used only for the output header
Workflow
Step 1: Check OCR Availability
Call the MCP tool ocr__check_ocr_availability. If ready is false:
- Report which components are missing
- Include the install hint from the response
- Return immediately with error output (do NOT attempt extraction)
Step 2: Detect File Type and Route
From the file extension:
| Extension | Tool to call |
|---|---|
.pdf |
ocr__extract_text_from_pdf |
.png, .jpg, .jpeg, .tiff, .bmp, .gif, .webp |
ocr__extract_text_from_image |
| Other | Return error: "Unsupported format for OCR" |
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.
- yesterday First seen · 150 lines · 41 tokens per session scan A 28cfd6e44eac
ocr-reader is an agent published in the GitHub repository datacore-one/datacore (4 stars, last pushed today), licensed MIT. It adds 41 tokens to every session and 1,035 once invoked, about $0.0002 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 2026-09-03.
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