Multi-Server MCP Architecture: Routing, Isolation, and Control
An architecture guide for coordinating multiple MCP servers without creating tool collisions, permission sprawl, or shared failure domains.
Topic
#tool-use
58 articles — updated daily
MCP Sampling Explained: Model Calls Requested by Servers
A practical explanation of MCP sampling, including message flow, model preferences, tool use, approvals, security, and current design status.
MCP Roots and Filesystem Boundaries Explained
A security-focused guide to MCP roots, file URI validation, workspace boundaries, user consent, symlinks, and modern alternatives.
MCP Logging and Completion Utilities
A practical reference to MCP logging notifications and completion suggestions, including current status, security, and implementation boundaries.
MCP Elicitation: Requesting User Input Safely
A practical guide to requesting missing user input during MCP operations without confusing conversation, consent, or credentials.
What Is MCP? A Practical Guide for AI Agent Builders
A practical, beginner-friendly guide to the Model Context Protocol: its host-client-server architecture, tools and resources, request flow, trade-offs, and security boundaries.
MCP vs Function Calling: What Actually Changes?
A practical comparison of MCP and model function calling, including their boundaries, request flow, portability, security, and combined architecture.
How MCP Works: From User Request to Tool Result
Trace an MCP interaction from server discovery through model tool selection, host authorization, execution, and the final answer.
Testing and Debugging MCP Servers
A practical workflow for inspecting, testing, and debugging MCP servers from protocol exchange to downstream side effects.
MCP Tool Permissions and Least Privilege
A practical permission model for controlling which MCP tools users and agents can discover, call, and approve.
MCP Security Threat Model for Production Systems
A practical threat model for MCP hosts, clients, servers, tools, credentials, model context, and downstream systems.
MCP Prompt Injection, Tool Poisoning, and Data Exfiltration
A defensive guide to prompt injection, tool poisoning, confused-deputy risks, and data exfiltration in MCP systems.
Deploy and Observe a Remote MCP Server
A production deployment guide for remote MCP servers covering network boundaries, identity, scaling, observability, and rollback.
MCP Tool Design: Schemas, Descriptions, and Structured Outputs
Practical MCP tool-design guidance covering names, descriptions, JSON Schema, structured results, errors, permissions, and testing.
MCP Error Handling: Timeouts, Retries, and Cancellation
A reliability guide to MCP errors, deadlines, retries, cancellation, progress, idempotency, ambiguous writes, and observable recovery.
MCP Authentication and Authorization Explained
A production-focused guide to authenticating MCP clients and authorizing users, tools, resources, tenants, and downstream actions.
Build an MCP Client: Discovery, Routing, and Results
An implementation guide to MCP client connection management, tool discovery, model mapping, routing, result handling, and observability.
Build Your First MCP Server in Python
A step-by-step Python tutorial for building, running, testing, and hardening a small MCP server with a typed tool.
MCP Tools Explained: How AI Agents Take Action
A practical guide to MCP tools, including schemas, discovery, execution, approvals, errors, and safe production design.
MCP Transports: STDIO vs Streamable HTTP
Understand how MCP messages travel over STDIO and Streamable HTTP and how to choose the right transport for local and remote servers.
MCP Resources Explained: Giving Agents the Right Context
Understand MCP resources, resource URIs, templates, discovery, reading, subscriptions, and safe context selection.
MCP Prompts Explained: Reusable Workflows for AI Applications
A practical guide to MCP prompts, including discovery, arguments, message content, user control, safety, and design patterns.
MCP Lifecycle and Capability Negotiation
A current guide to MCP version compatibility, capability discovery, request metadata, feature use, and the transition from older handshakes.
Designing Agent Fallbacks and Graceful Degradation
Keep an agent safely useful when models, tools, data, or specialists fail—without fabricating success or silently weakening controls.
Production Monitoring for AI Agents
Turn traces, metrics, logs, and evaluations into selected production signals, thresholds, dashboards, and actionable alerts.
Caching Strategies for AI Agent Systems
Reuse expensive results only when identity, freshness, authorization, and side-effect semantics make reuse safe.
Model Routing for AI Agents
Choose models by task requirements, policy, quality, latency, and cost instead of sending every step to one default.
Rate Limits and Backpressure in AI Agents
Control overload before immediate retries turn constrained models, tools, or workers into a failure storm.
Scaling AI Agent Systems
Grow workload capacity safely by separating stateless runtimes from durable tasks and protecting constrained dependencies.
Context and Token Cost Optimization
Build focused model context that preserves decision-relevant information while removing repeated and irrelevant tokens.
Agent Latency Explained
Why multi-step agents feel slow, where elapsed time accumulates, and how to improve speed without breaking the task.
Deploying AI Agents to Production
A practical path from a local agent prototype to a controlled, observable, and reversible production service.
Handling Tool Failures in AI Agents
A practical, production-oriented explanation of tool failure handling, with examples, boundaries, trade-offs, and failure handling patterns.
Idempotency in Agent Workflows
A practical, production-oriented explanation of idempotency in agent workflows, with examples, boundaries, trade-offs, and failure handling patterns.
Retries, Timeouts, and Failure Recovery in AI Agents
A practical, production-oriented explanation of retries, timeouts, and failure recovery, with examples, boundaries, trade-offs, and failure handling patterns.
Reliable AI Agent Architecture
A practical, production-oriented explanation of reliable AI agent architecture, with examples, boundaries, trade-offs, and failure handling patterns.
Human-in-the-Loop for AI Agents
A practical, production-oriented explanation of human-in-the-loop control, with examples, boundaries, trade-offs, and failure handling patterns.
Sandboxing AI Agents
A practical, production-oriented explanation of sandboxed agent execution, with examples, boundaries, trade-offs, and failure handling patterns.
Tool Permissions and Least Privilege for AI Agents
A practical, production-oriented explanation of least-privilege tool permissions, with examples, boundaries, trade-offs, and failure handling patterns.
Prompt Injection in AI Agents
A practical, production-oriented explanation of prompt injection in tool-using agents, with examples, boundaries, trade-offs, and failure handling patterns.
AI Agent Security Explained
A practical, production-oriented explanation of the security model of an AI agent, with examples, boundaries, trade-offs, and failure handling patterns.
Observability for AI Agents
A production observability model for agent, model, retrieval, tool, sub-agent, and infrastructure signals—with privacy and redaction controls.
MCP Security and Permissions
A practical security model for MCP trust boundaries, authorization, least privilege, approvals, external content, backend credentials, and auditability.
MCP Client Architecture
Understand how an MCP host manages dedicated clients, discovers server capabilities, applies policy, invokes operations, and handles failures.
LLM Evaluation vs Agent Evaluation
LLM evaluation scores model outputs; agent evaluation measures the whole goal-directed system, including tools, state, constraints, reliability, latency, and cost.
How to Evaluate an AI Agent
A practical workflow for defining agent success, building evaluation datasets, capturing traces, scoring behavior, analyzing failures, and preventing regressions.
Building an MCP Server
A framework-neutral tutorial for designing, implementing, testing, securing, and deploying an MCP server over real backend systems.
AI Agent Evaluation Explained
Agent evaluation measures task outcomes, trajectories, tool behavior, constraints, safety, reliability, latency, and cost—not only final prose.
Agent Traces and Trajectories
Learn how traces and trajectories represent observable agent execution without requiring storage or exposure of private chain-of-thought.
Orchestrator vs Supervisor vs Router in Multi-Agent Systems
Compare orchestrators, supervisor agents, and routers by purpose, decision ownership, state responsibility, delegation, routing, and workflow control.
Agent Workflows and Orchestration Explained
Learn how agent workflows and orchestration coordinate steps, dependencies, branches, parallel work, retries, checkpoints, tools, agents, and humans.
Agent Handoffs, Delegation, and Sub-Agents
Learn how delegation, handoffs, and sub-agents divide work while preserving task ownership, context, state, permissions, and reliable result contracts.
Agent Graphs and State Machines Explained
Learn how agent graphs and state machines make nodes, edges, branches, loops, checkpoints, transitions, retries, and terminal outcomes explicit.
Build Your First RAG Agent
Build a framework-neutral RAG agent with a controlled retrieval tool, attributable evidence, bounded loops, citation checks, traces, and layered evaluation.
RAG vs AI Agent: What’s the Difference?
A decision-focused comparison of RAG knowledge retrieval and AI-agent execution, including when a simple RAG pipeline is enough and when an agent is justified.
Single-Agent vs Multi-Agent Systems
Learn how single-agent and multi-agent systems differ, what extra coordination costs, and how to choose the simplest architecture that works.
Tool Use in AI Agents: How Agents Act Beyond the Model
Learn how AI agents select tools, prepare arguments, execute functions and APIs, observe results, recover from errors, and stay within safe permission boundaries.
What Is an AI Agent? A Practical Mental Model
A practical explanation of what makes an AI agent different from a chatbot or fixed workflow, and how the agent loop turns model reasoning into action.