VerifyAX MCP
OfficialRelated Servers
Alternatives to VerifyAX MCP
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityCmaintenanceEnables searching Verix records, retrieving record details, and running analysis workflows through natural language via the Verix API.MIT

Coval MCP Serverofficial
AlicenseAqualityAmaintenanceEnables AI assistants to interact with Coval's evaluation platform for launching and monitoring evaluation runs, managing agents and test sets, and retrieving evaluation metrics.1817 npm3MIT- AlicenseNot gradedqualityCmaintenanceExposes the full Telnyx API as Model Context Protocol tools and enables deploying autonomous AI voice agents from a YAML spec.MIT
- AlicenseNot gradedqualityBmaintenanceEnables LLM evaluation and observability by uploading documents, building test sets, running RAG pipelines, and automatically scoring answers for groundedness, hallucination risk, retrieval quality, latency, and cost, with tools exposed to MCP-compatible clients.1MIT
- AlicenseAqualityFmaintenanceEnables AI agents to verify email addresses, clean lists, run bulk verification jobs, and manage a Verifly account through natural language, without writing HTTP calls.154 npmMIT
- FlicenseNot gradedqualityDmaintenanceEnables SQL query execution and knowledge base search via RAG agent through two tools.-
TDQS
Scored across 14 tools
The three tag-discovery tools (list_compatible_tags, recommend_scenario_tags, search_scenario_tags) share an overlapping goal but their descriptions clearly differentiate full-catalogue filtering, LLM-ranked recommendation, and embedding-based search. Most other tools target distinct resource/action pairs (scenario, agent, run, cost/usage).
All tool names use a consistent snake_case verb_noun pattern: delete_scenario, list_agents, register_agent, generate_scenario, evaluate_agent, get_run_details, preview_run_cost, etc. Deviations are minor and still follow the same convention.
14 tools is well within the ideal 3–15 range for a platform covering agents, scenarios, tags, runs, evaluation, and usage. Each tool appears to earn its place by covering a distinct lifecycle operation or query type.
Core create/list/delete operations exist for agents and scenarios, plus run evaluation, cost preview, and usage summary. However, update operations for agents and scenarios are missing, and there is no get_scenario or get_agent detail tool—only list endpoints—creating notable gaps for managing existing resources.