"Information about guardrails" matching MCP servers:

An MCP server that exposes 43 domain tools behind 4 meta-tools, applying guardrails such as budgets, rate limits, schema validation, human-in-the-loop confirmation for mutations, PII redaction, and an append-only audit log while reducing context window usage.

High-frequency (~60Hz) in-browser behavior tree execution engine for autonomous AI agents. Reactive priority interrupts, safety guardrails & deterministic action replay over MCP.

A FastMCP server for Cursor platform APIs enabling spend guardrails, cloud agent monitoring, and inter-agent inbox messaging.

Enables AI coding agents to offload large downloads to AB Download Manager with multi-threaded acceleration, pause/resume, and queueing, while enforcing sandboxing and security guardrails.

Provides AI assistants with real-time access to the AGNO framework documentation by enabling them to browse, search, and fetch documentation pages. This server allows users to query information about AGNO's Agents, Teams, and Workflows directly through MCP-compatible clients.

Enables MCP-capable agents to perform local web research under a declared plan with session budgets, user-approved limit extensions, and guardrails for safe searching, page fetching, content extraction, and audit logging.

Enables coding agents to compact conversation contexts verbatim, make fast decisions through choice, boolean, and rubric scoring, and enforce command safety guardrails.

Enables AI agents to author and manage workflows, agents, tools, skills, policies, and reference docs in an Axonity tenant via the public REST API, with guardrails preventing direct publishing and secret exposure.

Tenant-bound MCP server for agent spend authorization, policy guardrails, evidence, escrow settlement, and portable receipts. Works with Cursor and Claude Code via stdio; npm (@paybond/kit) and Python (paybond-kit).

NexaLayer MCP Server — a Model Context Protocol server that lets AI agents create and manage dynamic and static proxy sessions with budget guardrails.

Enables AI agents to monitor live token consumption and rate-limit windows across providers and assess whether expensive actions are justified before exhausting quotas, with optional budget guardrails.

MCP server enabling AI agents to control a real Linux browser with live view, human takeover, and safety guardrails.

A persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.

Connects MCP hosts and LangChain agents to a local WAHA (WhatsApp HTTP API) instance, exposing tools for session management, chats, messages, contacts, and sending text, images, or files. It also provides an operational skill prompt covering lifecycle, chat ID formats, send guardrails, media rules, and error handling.

The AgentOps MCP server provides access to observability and tracing data for debugging complex AI agent runs. This adds crucial context about where the AI agent succeeds or fails.

Enables Claude Code and Claude Desktop to delegate bounded, disposable tasks to local or cheap cloud models, with model scoring, guardrails, and cost-saved tracking.

Enables pre-execution security checks and sandboxed execution for AI agent workflows, including prompt-injection detection, MCP rugpull and sampling abuse protection, untrusted tool-output sanitization, loop circuit breaking, SQL AST validation, PII redaction, and ephemeral Python execution.

A secure MCP server enabling tool calls (kb_search, read_doc, publish_report) through a zero-trust CapabilityBroker with OWASP LLM Top-10 guardrails and human-in-the-loop approval.

A self-hosted registry and MCP server for reusable AI-agent skills that enables agents to discover, retrieve, and install skills with guardrails.

Routes natural language queries to appropriate MCP tools and planners with high-precision semantic matching and safety guardrails. Supports multi-intent detection, task planning, and strict role-based filtering to prevent misexecution or unauthorized access.

Enables MCP-compatible agents to arbitrate typed state-action schemas, dispatch routine actions deterministically, and apply pre-escalation filters that reduce unnecessary frontier LLM calls. It supports low-latency, schema-enforced routing with safety guardrails and local-first memory retention.

Enables personal AI agents to sense ambient attention cues, compute intent probabilities, and execute reversible guarded dry-run actions without prompting. It routes routine reflexes through a fast System-1 decision layer backed by decaying episodic memory and strict safety and privacy guardrails.

Enables AI agents to connect to tools and data through a governed MCP endpoint with agent identity, scoped permissions, guardrails, and full audit trails. Access is user-controlled and can be revoked instantly, with every tool call logged by agent identity and timestamp.

Maple is a unified MCP server for agent observability, safety control, and behavior evolution, acting as a monitoring and auditing layer for high-agency agents. It enables developers to capture session timelines, replay risky branches, detect anomalies using ML, and enforce action guardrails.

Exposes identity, tools, workflows, guardrails, and evaluation as MCP tools — so any AI agent can read and write your ecosystem programmatically.

A server that enables AI models to access user geolocation information via EdgeOne Pages Functions through the Model Context Protocol.

Enables managing RunPod compute for the Learning-to-Swim replication through natural language, including pod lifecycle, SSH/rsync file transfer, detached training jobs, and supervised runs with cost guardrails.

A transparent message ferry between Claude Desktop and Claude Code CLI, enabling task delegation with any MCP configuration without the bridge needing to know about specific MCPs.

Enables hosts like Claude Code, Codex, Antigravity and Grok to dispatch work to DeepSeek Harness agents as native subagents with live progress, tier/effort presets, escalation, workspace guardrails and a jobs board. It also bridges multimodal capability into the text-only harness, letting workers read images and generate them.

Enables auditing MCP configuration token usage, compressing tool catalogs, pruning bloated JSON responses, and optionally persisting compressed profiles under read-only defaults and safety guardrails.