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Related Servers

Alternatives to Servonaut

No user-submitted related servers found.

    Related Servers

    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables AI agents to manage remote servers via SSH, including command execution, multi-host batch operations, SFTP file transfer, background job handling, DevOps diagnostics, port tunneling, and safety guardrails like high-risk command blocking and read-only mode.
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables AI agents to securely connect to and manage remote servers via SSH, with tools for running commands, file operations, log inspection, service management, and system monitoring under configurable access controls and audit logging.
      12
      8 npm
      1
      MIT
    • A
      license
      B
      quality
      A
      maintenance
      Provides policy-driven, auditable SSH access to server fleets for AI assistants with zero-trust security controls, command whitelisting, and comprehensive audit logging to safely manage infrastructure.
      13
      27
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI assistants to securely execute SSH commands on remote servers with connection pooling, session isolation, and a web audit panel.
      3
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enables AI tools to securely inspect remote servers by listing directories, reading configuration files, and performing authorized actions through permission controls, human-in-the-loop approvals, and audit logging. It acts as a bastion that never reveals raw SSH credentials or root passwords to the model.
      1
      MIT

    TDQS

    B3.4/5.0

    Scored across 69 tools

    Disambiguation3/5

    Several tool pairs overlap significantly: build_server_memory and refresh_server_memory are explicitly functionally equivalent, ip_ban_set and block_ip both ban IPs via WAF/SG/NACL, and check_status vs get_server_info vs get_server_memory all describe an instance at different granularities. The verbose cross-referencing descriptions save it from a lower score, but an agent could still easily misselect between these pairs.

    Naming Consistency4/5

    Snake_case verb_noun is the dominant pattern with clear prefix families (aws_, s3_, docker_, cloudwatch_, db_), making the namespace highly predictable and scannable. Minor deviations: build_server_memory vs refresh_server_memory are functionally identical despite implying different purposes, and docker_ps breaks verb_noun in favor of the familiar CLI command.

    Tool Count2/5

    69 tools is far too many for a single MCP surface and will strain an agent's ability to select among them reliably. The count is partially justified by the genuinely broad domain (server management, AWS, S3, Docker, DB, security), but the server would be much more coherent split into several focused servers.

    Completeness4/5

    The surface covers the full operational lifecycle: instance management, a complete memory build/refresh/recall system, S3 CRUD, EC2 lifecycle with all prerequisite lookups, a Docker diagnostic suite, DB setup and querying, and a coherent abuse-response workflow (enrich, describe ingress, block, rate-limit). Minor gaps exist (no non-AWS instance provisioning, no S3 policy/versioning, no general CloudWatch metrics), but aws_call and api_request act as escape hatches.

    Maintenance

    ActivityActive
    ResponsivenessNo issues