$ loading_
Coordinates test runs and uncovered-symbol analysis to raise code coverage.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "coverage-orchestrator-mcp" yet — see the docs or source repo.
Analyze current workspace coverage bottlenecks and coordinate relevant test runs to push coverage toward the target threshold.
A plan for which tests to run and how to improve coverage.
Combine workspace test execution results with uncovered-symbol analysis to list the code symbols most worth adding tests for.
A list of uncovered symbols and prioritized test suggestions.
After each test run, re-evaluate uncovered areas and coordinate the next tests until coverage approaches the target.
Iterative test actions and coverage improvement results.
When coverage falls below a target, developers can use it to coordinate test execution and identify priority gaps through uncovered-symbol analysis. This makes coverage improvement more targeted.
Before a release or regression pass, a team can identify key uncovered areas and drive the related tests to reduce the risk of misses.
It coordinates workspace test execution and uses code-tree-mcp uncovered-symbol analysis to push test coverage toward a target threshold.
From the description, it relies on workspace test execution and uncovered-symbol analysis from code-tree-mcp. For more details, see the source repository.
Analyze Java branch coverage and pinpoint uncovered lines by test class.
Store coverage snapshots locally and let LLMs query coverage history efficiently.
Orchestrate multi-model agents for plan-code-review-deliver workflows.
Build custom analysis MCP tools via JSON with built-in safety and quality controls.
Analyze code structure and Git history while drastically reducing AI token usage.
Orchestrate multiple MCP server tools for complex Python workflows with logic.