genpark-hybrid-bounding-box-ocr-table-reconstructor-skill
# genpark-ocr-table
Group supplied OCR text boxes into left-aligned table rows and columns. No image OCR is performed.
Python 3.9+; standard library runtime; MIT license.
## Install and run
Download `genpark-ocr-table.mcpb` from [GitHub Releases](https://github.com/Alpha-Park/genpark-hybrid-bounding-box-ocr-table-reconstructor-skill/releases/tag/v1.0.1) and install with an MCPB-compatible client. Python must be installed and available as `python`.
Alternatively clone this repository and configure an MCP stdio server with command `python` and arguments containing the absolute path to `mcp_server.py`.
[Smithery listing](https://smithery.ai/servers/krispang1020/genpark-ocr-table)
## Tools
- `reconstruct_table_grid`
- `format_to_markdown`
- `run_benchmark_table_reconstruction`
Run `python -m unittest discover -s tests` for regression checks. The official MCP SDK integration check uses the development dependency `mcp`: `python tests/check_mcp.py`.
## Limitations
These are deterministic helpers operating on supplied structured data, not machine-learning models. Input and output remain in the local process. No hosted endpoint, automatic file access or network access is required. State lasts only for the current process. Benchmark tools run synthetic examples in isolated state; their status is not a production-quality certification.
Rows use an 8-pixel vertical tolerance; columns use left-edge alignment with a 20-pixel tolerance. Merged cells, rotated text and centered layouts are not supported.
TDQS
Scored across 3 tools
Each tool clearly targets a distinct phase of the OCR-to-table pipeline: grid reconstruction, markdown formatting, and benchmarking. There is no overlap in purpose; an agent can easily tell them apart.
All tool names use snake_case and clear action-oriented phrasing, which is consistent. However, the verb pattern varies slightly (reconstruct vs. format vs. run_benchmark), making it slightly less uniform but still highly readable.
With only 3 tools, the server is focused but may feel thin for a complete hybrid OCR table reconstruction skill. It covers essential steps but lacks utility functions like preprocessing or validation, making the count borderline.
The set covers the core workflow: reconstructing the table grid, formatting output to markdown, and running benchmarks. Missing elements like input preprocessing or table extraction might be handled externally, but the surface is largely complete for the stated purpose.