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Manage task records and seeded documents locally through controlled MCP tool functions.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "ContextForge MCP Lab" yet — see the docs or source repo.
Use ContextForge MCP Lab to create a set of test task records and import 20 sample documents from local JSON seed files, then return a summary of the import results.
A summary of created tasks and documents, import counts, failures, and status details.
Query all task records with pending status, filter items with priority greater than 3, update them to in-progress, and output the update list.
A list of matched tasks, their updated statuses, and the success or failure of each operation.
Check whether document records in SQLite match the JSON seed data, list missing, duplicate, or field-mismatched entries, and provide repair suggestions.
A consistency report with problematic entries, issue types, and recommended fixes.
Build, debug, and manage software tasks with natural language across LLMs.
Generate production-ready MCP servers from databases or OpenAPI specs quickly.
Turn any OpenAPI spec into a working MCP server.
Run deep research and grouped tasks in parallel for faster execution.
Auto-generate MCP servers so AI can query data sources without code.
Delegate coding tasks to Codex via MCP with security and result checks.