MCP Discovery refers to the process used to find, identify, and catalog MCP-enabled resources, tools, and services within an AI ecosystem that uses the Model Context Protocol (MCP). It’s the capability that lets an AI system or security team automatically locate all of the MCP servers, endpoints, APIs, and tool definitions that are available for use by agents or applications.
The goal of MCP discovery is to build a comprehensive inventory of the environment so that AI agents can dynamically surface contextual capabilities, and security teams can govern and monitor access to those capabilities.
MCP itself (short for Model Context Protocol) is an open standard developed to standardize how large language models (LLMs) and AI agents connect with external tools, data sources, and services, solving the problem of custom, brittle integrations by providing a common protocol for discovery, invocation, and communication.
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MCP discovery is typically carried out in four stages:
Scanning: Analyzing cloud, on-prem, and hybrid environments to find MCP-enabled endpoints. Tools look at configurations, network traffic, and system processes to build a base layer of known MCP resources.
Identification: Categorizing the discovered components. It captures metadata like the tool’s role, location, and access level, making it easier to understand what each component does and who can access it.
Mapping: Links AI agents to the MCP tools and APIs they use. It shows how commands and data flow through the environment, which helps security teams understand the full context of each interaction and detect abnormal behavior.
Monitoring: Keeps the inventory accurate over time. Changes to MCP servers, such as new tools being added, configuration updates, or changes in access, are detected in real time. This ongoing visibility supports compliance, improves governance, and reduces operational risk.
Types of MCP Discovery Tools
Static Discovery Tools
Static discovery tools analyze the configuration layer of an environment before deployment. They scan infrastructure-as-code files, container definitions, deployment scripts, and code repositories to locate MCP-related configurations. This helps teams identify MCP services, tools, or endpoints that are being provisioned, along with their permissions and connectivity settings.
These tools are especially useful in development and staging environments, where they can catch potential issues (such as overprivileged access or incorrect API bindings) before they make it to production. They also assist in validating that infrastructure conforms to defined security and compliance policies.
Dynamic Discovery Tools
Dynamic discovery tools operate in active environments, continuously scanning for MCP tools, services, and endpoints across cloud, on-prem, and hybrid infrastructure. Unlike static tools, they detect components that are already running and respond to changes in real time.
These tools monitor infrastructure for new deployments, changes in configuration, or the addition of new services without requiring manual updates. This is critical in environments where AI agents may spawn new services dynamically or where teams are integrating external tools rapidly. Dynamic discovery helps eliminate shadow IT, ensures a complete asset inventory, and keeps the security posture current as the MCP environment evolves.
Agent-Centric Discovery Tools
Agent-centric discovery tools observe the interactions between AI agents and MCP-enabled components. They track which tools agents are calling, how often, under what context, and with what level of access. This level of visibility is important for validating that agents are operating within expected boundaries and not accessing unauthorized services.
By focusing on the behavior and access patterns of agents, these tools help detect anomalies like privilege escalation, lateral movement, or unapproved workflows. They are also valuable during incident response, allowing teams to reconstruct agent behavior and trace security events back to specific interactions.
Network-Based Discovery Tools
Network-based discovery tools analyze traffic flowing across the environment to identify MCP endpoints and communication paths. They do not rely on configuration files or agent behavior, making them useful for detecting hidden or unauthorized services that may not be exposed through other methods.
These tools inspect data packets, monitor service-to-service communication, and flag unusual traffic patterns. For example, they can detect an MCP component communicating with an external service that hasn’t been approved, or identify unexpected data flows between internal tools. Network-based tools are especially effective at uncovering misconfigured or compromised components that bypass traditional access controls.
A Typical Scenario: Discovering Tools Using MCP
In a practical MCP discovery scenario, a language model connects to an MCP server to retrieve the list of available tools, evaluate them, and decide how to use them based on a user query. This process demonstrates dynamic tool discovery in action.
For example, an OpenAI LLM might be configured with access to a server named releaseops, which provides three tools: one for listing services, one for getting deployment history, and one for comparing versions. These are exposed via the MCP interface, but the model is not hard-coded to know about their existence or how to use them, it determines both of those things at runtime.
For example, given a user question like "Why did checkout latency spike after yesterday's production deployment?", the model dynamically determines which tools it needs to call to answer. It might begin by employing list_services to confirm the exact service name and available environments, then call get_deploy_history to find the specific deployment that happened “yesterday” and capture the version change, and finally call compare_versions to see what actually changed between those versions before it explains the most likely cause in plain English.
This behavior is possible because the MCP protocol supports dynamic tool discovery, removing the need for static tool lists or manual integration steps. The require_approval: "always" flag also introduces a control layer, ensuring that every tool invocation is logged or mediated, useful for governance or human-in-the-loop workflows.
Notable MCP Discovery Tools
1. Obot
Obot provides a powerful MCP discovery and catalog layer that helps teams explore, understand, and connect to Model Context Protocol (MCP) servers in a secure, governed way. As MCP adoption grows, having a searchable directory of MCP endpoints — complete with documentation, schema insights, and connection information — accelerates development and reduces friction between AI agents and real-world systems. Obot’s built-in catalog supports both internal and third-party MCP servers and surfaces usage guidance so developers and business users can find the tools they need without guesswork or manual setup.
Key Features include:
Searchable MCP Catalog: A central directory of approved MCP servers with descriptions, capability summaries, and connection details that make it easy to discover the right tools for your workflows.
Automated Documentation & Insights: Live metadata and schema visibility help users and agents understand what each MCP server offers and how to use it effectively in real-world tasks.
Secure Access & Governance: Integrated access control and policy enforcement ensure teams discover only trusted MCP endpoints and connect safely using generated URLs or built-in clients.
Instant Client Connections: Users can grab ready-to-use MCP connection URLs for popular clients (e.g., Claude, Cursor, VS Code) or interact directly via Obot’s interface — shortening time to value.
Akto is a purpose-built MCP security platform that enables automatic discovery, monitoring, and protection of MCP-enabled servers and tools. It identifies both known and hidden (shadow) MCP components by analyzing traffic across cloud, hybrid, and on-prem environments. Once discovered, Akto continuously tracks agent interactions, including API calls, tool usage, and data flows.
Key features include:
MCP discovery: Automatically detects MCP servers and related APIs across all environments using traffic and code connectors
Real-time monitoring: Observes every MCP call and agent interaction, including execution context and parameter usage
Security testing: Scans endpoints for threats such as prompt injection, tool poisoning, and broken authorization
Dynamic inventory updates: Adds new MCP tools and services to the asset list without manual configuration
Proxy and threat blocking: Intercepts and blocks risky behavior using MCP-aware proxies and pattern analysis
Salt MCP Finder is a centralized discovery and governance platform that acts as the system of record for the agentic AI action layer. It identifies and tracks MCP servers and tools across their lifecycle (external exposure, codebase definitions, and runtime activity) to give security teams visibility and control.
Key features include:
Three-pronged discovery engine: Combines external (Salt Surface), code-level (GitHub Connect), and runtime (Agentic AI) discovery to cover the full MCP lifecycle
Salt surface: Detects public-facing or forgotten MCP servers before they become attack vectors
GitHub connect: Scans private repositories to find MCP configurations and shadow integrations before deployment
Agentic AI runtime monitoring: Observes live agent behavior, tool usage, and data flows to ensure runtime security
System of record for MCP: Maintains a unified, real-time inventory of all MCP servers, tools, and data sources
4. MCP Search Tool
MCP Search Tool is a fast, searchable interface for discovering and integrating Model Context Protocol tools into AI development workflows. It provides developers with access to a database of MCP tools, eliminating the need to sift through documentation or manually track resources. The platform also offers integration guides for popular IDEs like Cursor, VS Code, and Windsurf.
IDE integration: Step-by-step guides for integrating tools into Cursor, VS Code, Windsurf, Claude Desktop, and more
Curated tool database: Browse a vetted catalog of high-quality tools maintained by trusted developers and organizations
Real-time updates: Access the latest tool versions with continuous updates to the MCP tool inventory
Secure hosting: Host MCP tools on enterprise-grade infrastructure with built-in monitoring and auto-scaling
5. MCP Compass
MCP Compass is a discovery and recommendation service that helps AI assistants locate and understand Model Context Protocol (MCP) servers using natural language queries. It interprets user input and returns relevant MCP services along with metadata. This enables LLMs and developers to find tools for tasks without browsing through service registries or documentation.
Key features include:
Natural language search: Use conversational queries to find relevant MCP services quickly and intuitively
Detailed service metadata: View rich information about each discovered MCP service, including functionality and parameters
Live recommendations: Get real-time suggestions and insights as new MCP services become available
MCP server compatibility: Integrates into any AI assistant that supports MCP, enabling service-aware reasoning
Quick setup options: Deploy via npx, direct Node.js execution, or by updating config files for tools like Claude Desktop
MCP discovery is a key capability for operating agentic AI systems at scale. As environments grow more dynamic and tool-driven, organizations need continuous visibility into what MCP-enabled resources exist, how they are connected, and how agents interact with them. Effective MCP discovery supports safer autonomy, stronger governance, and better operational reliability by ensuring that both AI agents and security teams share a consistent, real-time understanding of the action layer.
Whether you’re piloting MCP internally or building a broader AI integration strategy, Obot helps you move from experimentation to operationalized MCP discovery with structure, visibility, and control. Visit our GitHub to try Obot today and see how you can transform MCP discovery from an ad-hoc process into a scalable, enterprise-ready system.
