Mipiti MCP Server
Related Servers
Alternatives to Mipiti MCP Server
No user-submitted related servers found.
Related Servers
AlicenseNot gradedqualityDmaintenanceEnables security work such as threat modeling, scanning, compliance checks, and remediation through AI assistants using the Model Context Protocol.MIT- AlicenseNot gradedqualityAmaintenanceEnables AI coding providers to run structured multi-agent workflows through MCP, including tracked tasks, document specs, handoffs, health gates, and audit evidence.29 npmMIT
- AlicenseAqualityAmaintenanceEnables AI agents to interact with the SCF Controls Platform for security compliance, including browsing controls, tracking implementation, managing evidence, assessing risks, and monitoring vendors via natural language.224534 npm3MIT
- FlicenseNot gradedqualityDmaintenanceEnables AI-powered threat modeling with tools for creating threat models, analyzing security threats, generating security controls, and validating architecture against best practices.-

Break Anythingofficial
AlicenseNot gradedqualityBmaintenanceConnects AI coding agents to security tooling so they can inspect authorized code, dependencies, container images, and infrastructure, confirm exploitability, and receive evidence-backed remediation plans.3,839 npmMIT- AlicenseNot gradedqualityBmaintenanceEnables AI agents and IDE assistants to run zero-config security audits, detect secrets and vulnerable dependencies, and apply adversarial verification to reduce false positives.2 npm1MIT
TDQS
Scored across 163 tools
With 163 tools, many overlap in purpose: assess_model vs check_control_gaps vs get_risk_view vs get_verification_report, query_threat_model vs get_threat_model vs get_composition, and several quality/attestation tools. Descriptions help but an agent must reason across many similar reads, making misselection likely.
Almost all names follow a snake_case verb_noun or noun_report pattern (e.g. get_controls, add_asset, submit_assertions, model_coherence_report). Minor deviations like set_control_objective_cal and get_findings_risks exist but the convention is readable and predictable.
163 tools is extreme even for a rich threat-modeling platform; the surface is far beyond a well-scoped set. This volume guarantees discovery and selection burden for agents.
The surface covers generation, editing, assessment, evidence, compliance, composition, behavioural modeling, findings, and review workflows. A few lifecycle operations may be indirect (e.g. description edits via refine rather than direct update), but overall coverage is extensive.