step-engineer
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- AlicenseNot gradedqualityCmaintenanceEnables MCP-capable coding agents to perform batched, token-efficient file and search operations with tolerant, automatically verified edits and diffs. It supports sandboxed workspace access, optional command execution, caching, and post-edit syntax/import checks.MIT
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Zof MCP serverofficial
AlicenseNot gradedqualityAmaintenanceGives a compatible coding agent read-only access to bounded local verification of changes inside a single host-chosen workspace, exposing six stdio tools that inspect capabilities, static checks, impact analysis, policy evaluation, evidence generation, and suggested tests without shell access, plugin loading, or repository upload.Apache 2.0- AlicenseNot gradedqualityBmaintenanceEnables delegating coding tasks to sandboxed agents, returning changes as auditable diffs for human review before applying or discarding them.1MIT
TDQS
Scored across 4 tools
Each tool maps to a distinct phase of the optimization job lifecycle: submit starts, get_status polls, get_result retrieves final output, cancel aborts. No two tools share the same action or resource, so an agent can easily choose the right one.
All four tools use snake_case with a consistent verb_noun pattern (submit_optimization, get_optimization_status, get_optimization_result, cancel_optimization). The structure is predictable and unambiguous.
Four tools cover the essential async job lifecycle without redundancy. This is a well-scoped set for a focused optimization service.
The surface covers the full lifecycle: create, poll status, fetch results, and cancel. No obvious operation is missing for managing an optimization job, and the run_id-centric design avoids dead ends.