MartinLoop — Stop Runaway Loops, Bad Code & Token Waste
Related Servers
Alternatives to MartinLoop — Stop Runaway Loops, Bad Code & Token Waste
- AlicenseAqualityAmaintenanceThis server enables LLMs to retrieve and process content from web pages, converting HTML to markdown for easier consumption.190,569MIT
- AlicenseAqualityNot gradedmaintenanceEnables browser automation through Playwright using accessibility tree snapshots instead of screenshots. Supports web scraping, form interactions, testing, and connecting to existing browser sessions with logged-in accounts.8234,622 npm5-
- AlicenseAqualityCmaintenancePython MCP server for GitHub operations, providing native tool integration with Claude Code and other MCP clients. It enables managing issues, pull requests, CI status, milestones, and batch operations via natural language.15MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI agents to access and search up-to-date Solana documentation, get canonical spec references, and fix Anchor/Pinocchio Solana programs via MCP tools.15 npm81MIT
Related Servers
- AlicenseNot gradedqualityAmaintenanceLocal Cost Guard and runtime guardrails for AI agents in Claude Code.126 npmMIT

agentguardofficial
AlicenseNot gradedqualityCmaintenanceEnforces policy controls for AI agents, including spend limits, action approvals, kill switch, scoped credentials, dry-run diffs, loop prevention, and auditable hash-chained logs.MIT- AlicenseNot gradedqualityDmaintenanceMCP Guard Server - Budget control, approval workflows and audit logging for AI agents (Claude Code, Cursor, ChatGPT)MIT
- AlicenseNot gradedqualityBmaintenanceStop runaway agents. One gateway in front of all your agent traffic — MCP tools and LLM models — one budget, one kill switch. Sits between AI clients (Cursor, Claude Desktop, Windsurf) and remote MCP servers / OpenAI-compatible model APIs; hard token budgets; a single static Go binary.1Apache 2.0
- AlicenseBqualityAmaintenanceGovernance runtime for AI agents: a guard tool evaluates risky actions against policy before they execute (block / warn / require human approval), approvals route to a human queue, and every action becomes a replayable decision record with per-action spend tracking. Runs over stdio via npx @dashclaw/mcp-server; works with Claude Code, Codex, LangChain, CrewAI, or any MCP host.68306MIT
- AlicenseNot gradedqualityFmaintenanceGovernance kernel for AI agents — policy enforcement, code safety verification, multi-model hallucination detection (CMVK), trust attestation (IATP), and immutable audit trails. Works with Claude Desktop, Cursor, and any MCP client.74MIT
TDQS
Scored across 24 tools
Several tools have overlapping purposes: martin_get_run, martin_status, martin_run_dossier, martin_dossier, and martin_inspect all read run data with subtle differences in format and depth. The descriptions try to disambiguate with 'do not use' guidance, but an agent could easily confuse martin_dossier vs martin_run_dossier vs martin_get_run.
Most tools follow a consistent martin_<verb>_<noun> pattern (martin_get_run, martin_create_pr, martin_review_pr). Minor deviations exist: martin_doctor, martin_arcade, and martin_arcade_status use noun-only or different structures, and martin_status vs martin_get_status inconsistency appears in descriptions.
24 tools is at the high end of acceptable for a complex run-management system, but the count feels inflated by near-duplicate read surfaces (dossier, run_dossier, get_run, inspect, status, logs, verification_results). A leaner set of 15-18 tools would cover the same functionality.
The lifecycle is well covered: estimate, plan, preflight, run, pause/cancel/continue, status, verification, eval, PR creation/review. Minor gaps include no explicit tool for listing attempts across runs or deleting/archiving runs, but agents can work around these.