Coala: a standard-based framework for converting CWL-described command-line tools into agentic toolsets
Year: 2026
Type: article
Abstract: SUMMARY: Large Language Models (LLM) can orchestrate computational analyses through agentic systems, but scaling their toolsets remains a barrier because tool definitions are often hard coded into agent implementation. We developed Command-line LLM-agent Adapter (Coala), a standard-based framework that bridges the Model Context Protocol (MCP) and the Common Workflow Language (CWL). Coala turns CWL tool descriptions into MCP-compatible, LLM-accessible schemas, treating tool definitions as data rather than code. Tools are then executed in containerized environments through a generic MCP server, which separates the agent's reasoning from tool execution. This framework improves reproducibility, reduces ongoing maintenance burden, and enables interactive access to local command-line tools through natural-language queries. AVAILABILITY AND IMPLEMENTATION: Coala is available at https://coala.info and is openly developed on GitHub: https://github.com/coala-info/coala.
Source: Bioinformatics
Authors: Qiang Hu, Qianqian Zhu, Hong Zhang, Tao Liu, Song Liu
Institution: Roswell Park Comprehensive Cancer Center
Language: English
Subfield: Information Systems and Management
Field: Decision Sciences
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $3,989
OpenAlex ID: W7204267335
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