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Lint AI agent work with YAML contracts to preserve project conventions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "MCP Contractor" yet — see the docs or source repo.
Please validate the agent-generated changes against this YAML contract. Focus on dependencies, business rules, and exports, and list violations with suggested fixes.
A contract lint report showing what complies or violates the rules, with fix suggestions.
Draft a structured YAML contract for this new module, defining allowed dependencies, key business rules, and exported interfaces so future AI agents can modify it safely.
A clear YAML contract draft that can serve as the rule basis for agent execution and linting.
Review these AI agent code changes against the contract and determine whether they introduce undeclared dependencies, break business rules, or alter exports that should remain unchanged.
A risk review identifying potential breaking points and the contract rules they violate.
When developers let AI agents modify project code, they can use YAML contracts to define dependencies, business rules, and export boundaries in advance, reducing the risk of breaking conventions.
Teams can encode project conventions as structured contracts so different agents or automation flows follow the same rules, reducing style and interface drift.
Before merging AI-generated changes, use the contract to check whether they violate dependency limits, business constraints, or export rules to support quality control.
It is a contract linter for AI agents. It uses structured YAML contracts to define dependencies, business rules, and exports so agents can work without breaking project conventions.
The provided description says it uses structured YAML contracts as the rule format. For exact fields, syntax, and examples, see the source repository.
Based on the description, it focuses on whether AI agents follow project dependencies, business rules, and export conventions, not just general code style issues. For more detailed differences, see the source repository.
Create, sign, and enforce binding agent agreements with deadlines and penalties.
Coordinate multiple AI agents on software projects with shared tasks and context.
Get intelligent, context-aware code reviews and improvement suggestions with MCP.
Coordinate specialized AI agents for software development, review, testing, and task tracking.
Build effective AI agents with MCP and simple workflow patterns.
Gives AI agents project conventions to keep development consistent across teams.