Speakeasy vs Workato MCP: Governing the AI tools employees already use

A developer adds a local MCP server to Claude Code and starts passing it customer records. Does your MCP platform know? Speakeasy finds the server from the session's tool calls and opens an access review, where you approve it for selected people or roles. Workato describes itself as an AI control and execution plane, and its MCP Registry and Gateway govern the servers teams compose, install, or proxy through Workato.

Choose Speakeasy to govern the AI clients and MCP servers your employees already use. Below, we compare the two on approving MCP servers, giving every agent an owner, and enforcing policy.

MoonPay runs Speakeasy in production across more than 200 MCP servers and 60,000 agent sessions. A 30-day proof of concept led to a company-wide rollout, with Okta-backed permissions by team, server, and tool, and unsanctioned MCP servers blocked by default. Read the MoonPay case study.

Speakeasy vs Workato MCP comparison

Enterprise readiness and track record

Speakeasy's gateway runs in production at companies with large, security-sensitive MCP estates. MoonPay's security operations team describes the change directly:

"Employees were connecting MCP servers we had no inventory of. Now every server runs behind the gateway, scoped by team and role, and anything unsanctioned is blocked by default."

Thierry Dang, Security Operations, MoonPay

Google, Fivetran, PlanetScale, LaunchDarkly, and Polar run MCP servers built on Speakeasy:

• Google moved the Gemini API SDKs to Speakeasy after its previous generation vendor was acquired and shut down, then built the Gemini Docs MCP server with Speakeasy on the same foundation. Google chose Speakeasy for continuity, on the commitment that Speakeasy would open source its generators so the tooling behind Gemini's SDKs and MCP server could not disappear again. The Docs MCP server gives coding agents current Gemini API documentation instead of stale training data, and ships pre-bundled in Antigravity.
• Fivetran exposed its Connector SDK and platform APIs as a hosted MCP server without spending weeks on infrastructure, an OAuth server, and client compatibility testing. Fivetran's Eli Davis calls Speakeasy's AI control plane "indispensable in enabling Fivetran's AI transformation."
• PlanetScale grew usage of its MCP server 44 times over in five months.
• LaunchDarkly built an MCP server that lets agents create feature flags, configure targeting rules, and manage rollouts.
• Polar moved from a local MCP server to a hosted one that turns a plain-language pricing description into a working billing setup.

Support is measured against service-level commitments rather than best effort. Speakeasy has met every response-time commitment across its support tiers, and 90% of support requests are answered in under 24 minutes.

Why choose Speakeasy as your AI control plane over Workato MCP?

Approve the servers employees already use

When employees connect MCP servers on their own, Speakeasy turns that usage into an approval queue. The shadow MCP inventory lists every server seen in agent sessions, including local servers launched on a laptop, and shows how many calls and people each one has. Workato's MCP Registry starts from the other end: teams publish servers into it.

An access review lets an admin approve a server for specific people or roles, and a blocking policy stops the rest. Once you add a server to the gateway, plugins assigned by role deliver it to Claude Code, Cursor, and Codex. In Workato, each employee finds the server in the registry and connects their own client.

Speakeasy's review holds the server's authentication and requested scopes, declared tools, domain and registry history, advisories, and observed users. Each decision preserves the evidence snapshot and rationale. Approved servers are rechecked daily for new tools, broader scopes, and changed authentication; those changes trigger re-review. A blocked employee's access request joins that history, so approving an exception does not lose the reason the server was blocked.

Delivery requires the marketplace and client setup, or enrolled devices running the device agent. Plugin assignments control managed device delivery, not who can install a published marketplace bundle; gateway permissions still govern calls. This keeps the distribution mechanism separate from the access decision.

For internal APIs, Speakeasy generates tools from the OpenAPI document your team already maintains, while Workato builds them from API collections or recipes.

Keep a growing catalog out of the context window

Speakeasy's gateway endpoint gives an agent one URL, then lets it discover servers and fetch only the schemas needed for the task. Each server keeps its own access and upstream authentication. The benefit is deferred schema loading across the catalog, not just combining URLs.

Workato recommends focused MCP servers with 5–8 tools, says 8–12 is reasonable, and suggests considering a split above 15. Those are design guidelines, not hard limits. Speakeasy puts catalog discovery at the gateway so a platform team can add approved systems without relying only on small, manually divided tool lists.

Give every agent an owner

When a team runs an automated agent, Speakeasy gives it its own agent identity, with one human owner, a policy that caps what it can do, and its own credentials. Speakeasy records the agent's tool calls and sessions under its identity, so the audit log shows what the agent did. Workato's headless MCP access uses named API tokens issued per MCP server; that token-based path cannot use Verified User Access to execute with the signed-in employee's backend permissions.

Every agent request also has to pass its owner's current permissions, so an agent can never do more than its owner. The agent loses a permission as soon as its owner does, and if the owner leaves, Speakeasy blocks the agent until someone reassigns it. Workato's Verified User Access runs tools as the signed-in user, but it doesn't work with token-based access.

For interactive access, both platforms can use the person's upstream identity. Speakeasy brokers OAuth credentials per upstream service and keeps the client's platform token separate from backend credentials. Its OAuth authorization server supports PKCE sign-in for servers that do not implement modern client authentication themselves. Workato's Verified User Access uses the employee's connection, subject to its exclusions for token-based MCP access and nested recipe functions. Speakeasy extends the owner-bound permission check to automated agents on every request.

Enforce policy on the agents employees already run

Security teams need guardrails where employees already work. Speakeasy's guardrails scan prompts, assistant messages, tool requests, and tool responses in Claude Code, Cursor, and Codex sessions, as well as tool calls through selected servers on the MCP gateway. Each guardrail applies to everyone or to specific people or roles. In Workato, content guardrails, including PII handling for tool input and output, are configured on each Genie, while the MCP Gateway handles identity, tool access, and traffic limits.

For risks that depend on context, such as deletes against production, an admin writes the policy in plain language. Before enforcing it, the admin replays it against recent sessions to see what it would have flagged. Workato's closest equivalent is a list of denied topics on each Genie, tested by chatting with it in Test mode.

Speakeasy separates session policies from MCP-scoped checks. Hooks can apply supported session actions, including quarantine, while gateway policies log or deny the request or result. Gateway checks scan the first 50 KiB and allow a call through on oversized content or scan timeout; session prompt-based policies separately let an admin choose how judge errors behave. A destructive-action detector reporting completed activity is not the same as a permission denying that action before execution. The control-plane advantage is choosing the boundary, testing the policy, and managing the resulting finding together.

Beyond the gateway: enterprise AI governance

Investigate sessions and measure adoption

To investigate a failed tool call, you need the preceding prompt, the returned data, the next attempted action, and the policy finding.

Speakeasy's Agent Sessions view connects captured transcripts to tool arguments and results, risk findings, token totals, and cost. Transcript access requires explicit permission, and opening a transcript is audit-logged.

Watchdog groups repeated detections into ranked signals and shows the affected people and teams. Sensitive matches stay redacted until an investigator reveals them, with each reveal audited. A recurring false positive can become an exclusion without removing the detector. A real exposure can lead to a blocked server, a narrower role grant, or a stronger policy, with the session providing the context for that decision.

Session capture and tool-payload recording start off until an administrator enables them; stored sessions and tool logs have 90-day retention. OTLP exports provide separate routes for product telemetry, policy findings, and gateway tool logs, excluding sensitive content from new destinations by default. Workato also streams audit logs and records MCP operations. Speakeasy's additional value is the captured coding-agent conversation, risk triage, and policy response around the call, including instrumented local tools outside the Workato registry.

For enablement teams, Costs shows active users, AI clients, trends, and role- and employee-level breakdowns. Budgets, currently in preview with organization enablement, match people by department, role, or cost center, give each one their own allowance, and can block over-budget requests in coding agents. Spend is evaluated roughly every 30 seconds; requests are allowed if a rule cannot be evaluated. A rollout owner can see who is using the tools and where to investigate spend.

Maintain the instructions external agents use

Workato recipes implement the integration workflow. Speakeasy's Skills govern a different artifact: the reusable instructions loaded by employees' coding agents. Skills have immutable, content-addressed versions, side-by-side comparison, pinned or latest-version distributions, and restoration of earlier content without rewriting history.

Plugins package those instructions alongside approved MCP servers. Activation records show which versions are actually used; sampled efficacy, attributed cost, and agent feedback help a platform team decide whether to update or roll back. When a billing agent starts making poor tool choices, the team can inspect the instruction change as well as the API calls. Governing the recipe alone does not maintain the instructions in an external coding agent.

Cover supported activity beyond the registry

Speakeasy's AI integrations add Anthropic and OpenAI compliance data and Cursor usage and spend. Compliance imports are evidence after an event; Anthropic inference hooks and LiteLLM integrations provide separate pre-inference checks. The device agent extends configured instrumentation to Claude Code web and Cursor Cloud environments, with dedicated enrollment identities for shared sessions.

This coverage depends on deployed hooks, enrolled devices, or connected provider feeds. Once those are in place, an employee's local MCP activity joins the same review, investigation, and cost workflows as approved gateway traffic, rather than becoming visible only after the team publishes a Workato server.

Speakeasy vs Workato MCP feature comparison

Connect and distribute

Access control

Guardrails

Observe

Enterprise deployment and assurance

Speakeasy's platform security documentation lists SOC 2 Type II, ISO 27001, GDPR, and CCPA compliance. It also covers encrypted credentials, customer-managed encryption keys, and configurable storage of transcripts and tool data.

For private systems, internal MCP tunnels create an outbound connection from your network, so a private MCP server joins the governed path without inbound firewall rules. On the client side, Tailscale private access puts selected MCP endpoints on your tailnet. Setting an endpoint to Private only closes its public route, with no automatic public fallback.

When Workato MCP may fit

Workato MCP is a reasonable choice for a narrower set of needs:

• Agents that trigger existing recipes: Your team already operates the recipes and connections that implement the process, and the agent's job is to trigger it.
• Agents built as Genies: Agent Studio keeps content guardrails and Business Approvals, with assigned reviewers in Slack or Microsoft Teams, alongside your integration workflows.
• Compliance breadth: Workato lists SOC 1 Type II, SOC 3, ISO 27701, PCI-DSS Level 1, and HIPAA with BAAs, alongside SOC 2 Type II and ISO 27001.

If the priority is approving the MCP servers employees already use, giving every agent an owner, and enforcing policy in the AI clients your teams work in, the sections above show where Speakeasy is stronger.

Why Speakeasy is the better choice

• Governance reaches the coding agents your teams work in. Hooks and the device agent cover Claude Code, Cursor, and Codex, so policy applies where the work happens.
• Servers employees add on their own still get a review. Unapproved MCP servers, including local ones, show up from real usage, and you approve each for the people and roles that need it.
• Approved tools arrive by role. Plugins deliver servers and skills to each member's coding agents based on their role.
• Guardrails are tested on real traffic first. Replay a plain-language policy against recent sessions before you enforce it, and quarantine a session when the policy fires.
• Every agent has an owner. Each agent identity has a named owner, its own policy, and credentials you can revoke without touching anyone else's access.
• Cost follows people. See each member's spend, and set per-person budgets matched by department, role, or cost center.

If your governance program has to reach beyond Workato recipes to the coding agents, local servers, and instructions employees already use, Speakeasy brings those supported surfaces into one control plane.