Capability 02 of 04 · Infrastructure
MCP & Tool Integration
How agents connect to external tools, APIs, and internal systems via MCP (now universal standard) and plugins.
5
maturity levels
15
practices
15
guides
L1 · Stage 01Ad-hoc
Practices - what it looks like3 guides
- Agent uses built-in tools onlyZero MCP is the baseline state: your AI agent has no programmatic connection to any tool, system, or data source outside its training data.guide→
- Agent relies on public / general knowledgeWhen an agent operates with only public API knowledge, it answers questions about your codebase using information from its training data - open-source documentation, public GitHubguide→
- Integrations done by copy-pasteCopy-paste integration is the universal first approach to giving AI agents context: a developer encounters a problem, grabs the relevant information (an error message, a stack tracguide→
Criteria - what to measure
- 01Agents use their built-in tools
- 02Agents draw on their general / built-in knowledge
- 03Team is aware of MCP as a standard for agent-tool integration
- 04Integrations, if any, are manual (copy-paste between tools)
L2 · Stage 02Guided
Practices - what it looks like3 guides
- 1-3 basic MCP servers (Git, Jira, docs)The first practical MCP deployment replaces the most expensive copy-paste operations with programmatic connections.guide→
- Manual MCP setup per developerManual MCP setup per developer is the phase where MCP servers exist and work, but each developer is responsible for installing and configuring them independently.guide→
- Basic tool authorization; mcp-auth-proxy for OAuth/token-persistence issuesBasic tool authorization is the first deliberate access control layer on MCP tool usage: decisions about which agents can call which tools, under what conditions.guide→
Criteria - what to measure
- 011-3 MCP servers are configured (e.g., Git, Jira, documentation)
- 02MCP setup is documented but configured manually per developer
- 03Basic tool authorization is implemented (agents authenticate to MCP servers)
- 04MCP server configurations are shared via repository (not local-only)
- 05At least one MCP server provides internal documentation or codebase context
L3 · Stage 03Systematic
Practices - what it looks like3 guides
- MCP platform: centralized server managementA centralized MCP platform moves server configuration, deployment, and credential management from individual developer machines to organization-managed infrastructure.guide→
- Architecture MCP, Ownership MCP, SLA MCP; deep-system MCPs (pentester-mcp, windbg-mcp, Pepper for iOS runtime)Specialized MCP servers organized by knowledge domain represent the L3 evolution from general-purpose data access to structured organizational intelligence.guide→
- RBAC per MCP tool; lazy tool-loading / MCP Tool Search (cuts token overhead and live attack surface; unloading unused servers recovers 24%+ context)Role-Based Access Control per MCP tool means defining precisely which agents can call which tools, based on the agent's role, the task it's performing, and the data it's operating on.guide→
Criteria - what to measure
- 01Centralized MCP platform manages server provisioning, configuration, and lifecycle
- 02Domain-specific MCP servers exist (Architecture MCP, Ownership MCP, SLA MCP)
- 03RBAC controls which agents can access which MCP tools
- 04MCP server health is monitored with alerting on downtime
- 05New MCP servers go through a standardized review and onboarding process
L4 · Stage 04OptimizedMost teams aim here
Practices - what it looks like3 guides
- Toolshed model: 400+ tools behind one MCP (Stripe)The Toolshed model, pioneered by Stripe, consolidates hundreds of distinct tools behind a single MCP endpoint.guide→
- Agent discovery: agent knows what tools are availableAgent discovery is the capability for an agent to dynamically enumerate what tools are available in its current environment and adapt its behavior accordingly.guide→
- MCP governance: lifecycle, versioning, provenance-checked installs (PolicyLayer State of MCP: 42% of 2,031 servers expose a destructive tool, 96.1% never warn the agent); defend against Agentjacking (malicious tool/error output treated as trusted remediation); per-MCP cost attribution via /usageMCP governance means treating MCP servers as production services with the same lifecycle management, change control, versioning, and audit requirements as any other production softguide→
Criteria - what to measure
- 01Toolshed model: 400+ tools accessible behind a unified MCP gateway (Stripe model)
- 02Agent discovery: agents can query available tools and their capabilities at runtime
- 03MCP governance covers lifecycle management, versioning, and audit logging
- 04MCP tool usage analytics track which tools are used, by which agents, how often
- 05MCP server versioning allows rollback to previous versions without downtime
L5 · Stage 05Autonomous
Practices - what it looks like3 guides
- MCP as nervous system: bidirectional context flowAt L5, MCP is no longer just a protocol for giving agents access to tools - it is the real-time information backbone that connects every part of the software delivery system.guide→
- Production → MCP → Agent → Code → Deploy → ProductionThe Production-MCP-Agent-Code-Deploy-Production loop is the fully autonomous software delivery cycle: production systems detect a condition (anomaly, performance degradation, configuide→
- Agent-to-Agent Protocol (A2A) + MCP combinedThe Agent-to-Agent Protocol (A2A), developed by Google and adopted as an open standard, defines how autonomous agents communicate with each other: how one agent delegates a task toguide→
Criteria - what to measure
- 01MCP operates as a bidirectional nervous system: production data flows to agents, agent actions flow to production
- 02Full production loop: Production -> MCP -> Agent -> Code -> Deploy -> Production
- 03Agent-to-Agent Protocol (A2A) and MCP are combined for multi-agent coordination
- 04MCP latency for context delivery is under 500ms P95
- 05A2A protocol enables agents to discover and delegate to other agents without human configuration
From the Field
Recent releases, projects and discussions the AI Radar classified into this capability.
release
mcp-use/mcp-use
discovered
An evolutionary multi-agent collaboration platform based on AgentScope Java.
discovered
AI-powered travel hacking with points, miles, and award flights. Drop-in skills and MCP servers for OpenCode and Claude Code.
discovered
Howardzhangdqs/mcp-wechat-server
release
Skyvern-AI/skyvern
release
sourcebot-dev/sourcebot
release
upstash/context7
release
mem0ai/mem0
Which level is your team at in MCP & Tool Integration?
The criteria above are what we check in a facilitated assessment. Run it yourself to place this capability, see which gates you have passed, and compare it against the other 3 in Infrastructure.
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