2026 Comparative Analysis: MCP Tool Gateway and Registry Infrastructure for Governed AI Agents — Applied Technology Index

Executive Summary

MCP adoption is pushing agent infrastructure beyond single-server demos into tool discovery, tool packaging, session scoping, gateway routing, and runtime governance. The practical enterprise question is no longer whether an agent can call an MCP tool. It is whether an operator can decide which servers are discoverable, which tools are available to a given user or task, where credentials live, how a remote tool is authorized, and how tool execution is logged when a model chooses the action.

The public ecosystem is separating into three layers. The official MCP Registry is a metaregistry for server metadata, intended to make public MCP servers discoverable without hosting their package artifacts. Docker’s MCP Catalog and Toolkit turn discovery into local or desktop execution by packaging verified servers as containers, organizing them into profiles, and exposing a gateway to MCP clients. Cloudflare, Composio, and Arcade address adjacent production needs: remote MCP server deployment and OAuth authorization, session-scoped hosted MCP endpoints, large tool catalogs, per-user authentication, policy enforcement, and audit-oriented control planes.

For buyers, the emerging lesson is that a registry is not a gateway, and a gateway is not a full governance system. A registry answers “what exists?” A toolkit or gateway answers “what can this client reach right now?” Authorization and governance answer “who approved this action, under which scope, for which user, with what evidence?” Production agent programs should evaluate all three layers together instead of treating MCP support as a checkbox.

Key findings

MCP’s tool primitive standardizes model-facing actions, but the protocol alone does not decide which tools should be offered to an agent, which server package should be trusted, or which user authority applies to a tool call.
The official MCP Registry positions itself as a canonical metaregistry for public MCP server metadata; it references package registries such as npm, PyPI, NuGet, OCI, MCPB, and Docker rather than hosting binaries itself.
Docker’s MCP Catalog and Toolkit are the clearest public example of registry-to-runtime packaging: verified catalog entries, containerized local execution, profile-based grouping, a gateway for clients, OAuth handling for remote servers, and experimental Dynamic MCP tools for runtime discovery and server addition.
Cloudflare’s MCP documentation focuses on building and deploying remote MCP servers, especially Streamable HTTP connections with OAuth authorization. Its broader AI Gateway documentation is relevant as an inference observability and control layer, but public AI Gateway materials should not be mistaken for a full MCP tool registry.
Composio frames sessions as the scoped runtime context for an agentic run. A session ties together user identity, toolkits, authentication, connected accounts, execution state, and optional hosted MCP access.
Arcade publicly positions itself as an enterprise-ready actions runtime with agent authorization, a large catalog of agent-optimized tools across MCP servers, central governance, visibility filtering, version control, and OpenTelemetry audit logs. Those are vendor claims from public documentation and should be verified during procurement.
The governance gap is not basic connectivity. It is curation, tool-quality control, credential minimization, least-privilege scoping, prompt-injection resistance, approval UX, and audit correlation across a model trace, MCP server, downstream API, and human user.

Methodology

This analysis reviewed public documentation and technical source materials available on 21 July 2026. X and public developer discussion were used only for signal discovery around MCP gateways, registries, catalog growth, dynamic tool discovery, hosted MCP endpoints, and agent-action governance. Claims in this article are grounded in primary or technical sources: MCP specification and security documentation, the MCP Registry repository, Docker documentation, Cloudflare documentation, Composio documentation, and Arcade’s AI-readable documentation index.

Each system was assessed on six criteria:

Discovery model: whether the source provides canonical registry metadata, curated catalog entries, hosted tool search, client profiles, or runtime tool discovery.
Execution boundary: whether tools run locally, in containers, on a provider-hosted endpoint, behind a remote MCP server, or through a model-provider gateway.
Authorization and user scoping: whether public materials describe OAuth, connected accounts, scoped sessions, access policies, user IDs, per-action authorization, or identity-provider integration.
Governance and audit: whether the system exposes control-plane concepts such as profiles, visibility filtering, version control, logs, OpenTelemetry, policy hooks, rate limits, or administrative oversight.
Packaging and trust: whether server metadata, package provenance, SBOMs, signatures, ownership verification, or curated verification are part of the public design.
Operational risk: where documentation leaves open questions around tool quality, prompt injection, tool poisoning, stale metadata, secret handling, tenant isolation, plan-specific controls, or evidence available to security reviewers.

The comparison does not claim live conformance testing, security certification, independent benchmark results, private roadmap access, enterprise contract terms, or customer deployment outcomes. Vendor-provided counts and capability claims are treated as public positioning unless independently documented elsewhere.

Comparative Analysis Table

System	Primary role	Strength for governed agents	Main limitation	Best operator use
MCP tool primitive and security guidance	Protocol-level tool descriptions, invocation semantics, and security recommendations	Establishes the shared model-facing contract for tools while warning about tool poisoning, confused deputy risk, and authorization failures	Does not itself curate packages, operate a gateway, verify server quality, or enforce enterprise policy	Baseline design standard for any MCP-compatible tool surface
Official MCP Registry	Canonical metaregistry for publicly available MCP server metadata	Creates a neutral discovery layer and standardized `server.json` metadata for clients, subregistries, and package references	Preview/API-freeze status and metadata orientation mean buyers still need curation, package trust, runtime isolation, and policy enforcement	Ecosystem discovery, client integration, subregistry ingestion, and publisher metadata normalization
Docker MCP Catalog and Toolkit	Curated catalog plus Docker Desktop management interface and MCP gateway	Converts catalog entries into containerized local execution, profiles, client connectivity, and experimental dynamic discovery tools	Desktop/local emphasis may not satisfy centralized enterprise governance by itself; Dynamic MCP is explicitly experimental	Developers and platform teams standardizing safe local MCP execution across Claude, Cursor, VS Code, and similar clients
Cloudflare Agents MCP infrastructure	Remote MCP server deployment and OAuth authorization on Cloudflare Workers	Strong fit for HTTP remote MCP servers, Cloudflare Access, third-party OAuth providers, and edge-hosted authorization patterns	Provides building blocks rather than a turnkey cross-vendor tool registry or catalog-quality system	Cloudflare-native teams exposing governed remote MCP servers or securing agent-accessible services
Cloudflare AI Gateway	Inference traffic observability and control for AI applications	Useful adjacent control plane for analytics, caching, rate limiting, model fallback, and provider routing	Public AI Gateway docs focus on model-provider traffic, not MCP tool discovery or downstream tool authorization	Teams that need model-call governance alongside, but not instead of, MCP tool governance
Composio sessions and hosted MCP endpoints	Session-scoped tool runtime, authentication, connected accounts, and optional MCP endpoint	Lets teams scope tools to a user, toolkit, tool list, auth config, and execution state; hosted MCP exposes the same session to MCP-native clients	Broad default discoverability can be risky unless sessions are filtered; procurement must verify logging, tenant isolation, and compliance controls	Product teams needing many SaaS actions with per-user connected accounts and fast MCP or SDK integration
Arcade actions runtime	Enterprise action runtime with agent authorization, catalog, policy, and governance positioning	Public docs emphasize OAuth/token handling, per-action authorization, existing policy integrations, registry/version control, visibility filtering, and OpenTelemetry audit logs	Public details in `llms.txt` are high-level; buyers need deeper verification of controls, supported integrations, data handling, and pricing	Enterprises that want a managed control plane for many agent actions rather than hand-rolled MCP servers

Observed Profiles

MCP tools: the standard contract, not the whole control plane

The MCP tool specification defines how servers expose callable tools with names, descriptions, input schemas, optional output schemas, annotations, and execution results. This standardization is the foundation that lets an MCP host present tools to a model in a consistent way across providers and frameworks.

The security implication is that tool descriptions become part of the model’s decision surface. MCP’s security best-practices documentation warns about risks such as tool poisoning, rug pulls, shadowing, confused deputy attacks, token passthrough, and unclear user consent. A production gateway therefore needs more than a list of available tools. It needs trust decisions about server source, version, prompt-exposed descriptions, authentication boundary, allowed scopes, approval requirements, and monitoring.

For operators, MCP compatibility should be treated as an interoperability minimum. It should not substitute for package review, secret isolation, scoped authorization, allowlists, or tool-call audit logs.

Official MCP Registry: canonical metadata for public server discovery

The MCP Registry repository describes the registry as providing MCP clients with a list of MCP servers, like an app store for MCP servers. Its ecosystem-vision document distinguishes the registry specification from the official hosted registry at registry.modelcontextprotocol.io, which is intended as the authoritative repository for publicly available MCP servers.

The important architectural detail is that MCP registries are metaregistries. They host metadata about MCP servers, not the package code or binaries. A registry entry can point to package registries such as npm, PyPI, Docker Hub, NuGet, OCI, or MCPB packages. The standardized server.json format covers server identity, package location, runtime and transport details, environment variables, repository links, versioning, and extension metadata.

That design is strategically useful because MCP clients, subregistries, marketplaces, and internal catalogs can consume the same canonical metadata. It is not, by itself, enough for enterprise approval. Operators still need to decide which entries are trusted, which versions are allowed, whether package provenance is adequate, whether environment variables are secrets, whether the tool descriptions are safe, and whether a server can reach sensitive systems.

Docker MCP Catalog and Toolkit: registry-to-runtime packaging for local agents

Docker’s MCP Catalog is a curated collection of verified MCP servers packaged as Docker images and distributed through Docker Hub. Its documentation says the catalog includes verified servers with versioning, provenance, and SBOM metadata; partner tools; Docker-built and digitally signed local servers; and remote services that connect to external providers.

Docker’s MCP Toolkit then provides a Docker Desktop management interface for setting up, managing, and running containerized MCP servers in profiles and connecting them to AI agents. The toolkit emphasizes cross-client compatibility, integrated tool discovery, zero manual dependency setup, profile-based organization, and acting as a gateway for clients to access the servers in each profile.

Dynamic MCP is the most forward-looking part of the Docker model. The documentation says it lets agents discover and add MCP servers on demand during a conversation. When connected to the MCP Gateway, the client receives management tools such as mcp-find, mcp-add, mcp-config-set, mcp-remove, mcp-exec, and code-mode. Docker labels the feature experimental, and dynamically added servers are associated with the current session only rather than persisted to a profile.

The buyer implication is that Docker is turning MCP from manual local configuration into managed local execution. The caution is that dynamic tool addition increases the need for approval UX, policy controls, catalog trust, and post-hoc auditability. An agent that can add tools during a conversation can also expand its authority during a conversation unless the operator constrains the gateway.

Cloudflare: remote MCP servers and OAuth at the edge

Cloudflare’s Agents documentation describes building and deploying MCP servers on Cloudflare. It distinguishes remote MCP connections over the Internet using Streamable HTTP and OAuth authorization from local MCP connections using STDIO. The same documentation recommends focused tool design, narrowly scoped permissions, and detailed tool descriptions rather than wrapping an entire API schema.

Cloudflare’s authorization documentation says MCP uses a subset of OAuth 2.1 so users can grant limited access without sharing API keys. Cloudflare’s OAuth Provider Library can be used with Cloudflare Access, third-party OAuth providers such as GitHub or Google, an existing provider such as Auth0 or WorkOS, or Worker-managed authorization.

This makes Cloudflare most relevant for teams that want to expose remote MCP servers near their existing edge, Workers, Durable Objects, and Zero Trust controls. It is a server and authorization construction kit rather than a universal MCP catalog. Teams still need to define tool-level scopes, approval paths, downstream token storage, event logging, rate limits, tenant isolation, and what evidence an auditor can inspect after an agent action.

Cloudflare AI Gateway is adjacent rather than identical. Its public documentation describes observing and controlling AI applications with analytics, caching, rate limiting, model fallback, and provider routing. Those are useful controls for model traffic, but they do not replace an MCP-specific registry, package trust process, or downstream tool authorization layer.

Composio: session-scoped tool access and hosted MCP endpoints

Composio’s documentation defines a session as the runtime context for an agentic run. A session ties together the user, the available toolkits, authentication, connected accounts, execution state, logs, tool memory, MCP state, and workbench files. By default, a session can discover Composio’s catalog through meta tools, while developers can restrict available toolkits or tools when creating the session.

The MCP-specific documentation says every session can expose a hosted MCP endpoint when created with mcp: true. The same session backs both session.tools() and the MCP endpoint, so toolkit filters, auth configs, and connected accounts apply across both. Composio also documents a direct-tools preset for serving exactly listed tools through a single MCP URL without putting search or meta tools in front of them.

The strategic value is fast access to many SaaS actions with per-user connected accounts. The governance risk is default breadth. If every toolkit is discoverable unless restricted, a production team should make allowlists, OAuth scopes, connected-account selection, and direct-tool presets part of the deployment template rather than relying on an agent to choose safely at runtime.

Arcade: managed action governance positioned above raw MCP

Arcade’s AI-readable documentation positions Arcade as an enterprise-ready actions runtime for AI agents. It says Arcade enforces security policies on every action, executes across systems, and governs agents centrally in production. The same public index claims three major capabilities: agent authorization that handles OAuth, user tokens, API keys, secrets, IdP/DLP/SIEM/compliance policies, and per-action authorization; agent-optimized tools across MCP servers; and agent lifecycle governance with a central control plane, shared registry, version control, visibility filtering, and OpenTelemetry audit logs.

Those claims map directly to the missing enterprise layer around raw MCP servers. If a buyer has dozens of agents and thousands of actions, the hard part becomes policy consistency, version drift, audit completeness, and avoiding broad tool exposure. A managed runtime can reduce integration burden if its controls are real, inspectable, and compatible with the buyer’s identity and monitoring stack.

The caveat is that public summary pages are not enough for procurement. Teams should request detailed control documentation, supported authentication providers, data-retention terms, logging schemas, SOC or security evidence where applicable, failure-mode behavior, pricing, and the exact semantics of per-action authorization.

Buyer and operator implications

Separate discovery from authorization. A registry or catalog can tell an agent what tools exist, but authorization must decide whether this user, agent, task, and environment can invoke a specific action now.

Treat dynamic discovery as a permission escalation surface. Dynamic MCP and searchable tool catalogs are useful for flexible agents, but the ability to add a tool at runtime should be mediated by policies, user confirmation, or environment-specific allowlists.

Prefer narrow sessions and profiles. Profiles, direct-tool presets, and toolkit filters reduce model confusion and lower blast radius. For high-risk workflows, start with the smallest stable tool set and expand only after observing failures.

Verify package and server provenance. Metadata, SBOMs, signatures, ownership verification, and curated catalogs are useful trust signals, but they should feed an internal approval process. A signed container can still expose an overbroad or poorly described tool.

Keep credentials outside the model context. OAuth flows, connected-account stores, secret vaults, and short-lived tokens are preferable to copied API keys in prompts, config snippets, or local client files.

Log the chain of responsibility. Useful records should correlate the human user, agent or client, MCP host, MCP server, tool name, tool arguments or redacted argument class, approval decision, downstream API target, execution result, model trace, and request ID.

Evaluate AI gateways and MCP gateways separately. Model-provider gateways help with inference cost, rate limits, caching, and model observability. MCP tool gateways help with action discovery, authorization, package trust, and downstream side effects. Mature agent platforms often need both.

Limitations

This analysis relies on public documentation, AI-readable documentation indexes, and public repository materials. It does not include private product documentation, paid-plan feature matrices, enterprise contracts, live conformance tests, source-code audit of hosted services, or customer deployment evidence.

Vendor and project terminology differs. Public materials use terms such as registry, catalog, gateway, toolkit, session, runtime, action, tool, MCP server, MCP host, connector, auth provider, and control plane in overlapping ways. This article normalizes terms for operator usefulness, but implementation details can differ materially by product plan, SDK version, deployment model, and identity-provider configuration.

The MCP ecosystem is changing quickly. Registry status, API stability, server package formats, Docker Desktop features, Cloudflare Agents documentation, Composio session behavior, Arcade product capabilities, and enterprise governance controls may change after publication. Buyers should verify current documentation and request security evidence before standardizing on a tool-control plane.