Somebody in your company asked for Glean, which its own pages describe as AI-powered workplace search (glean.com/enterprise-search, checked October 2026). Somebody else already has Claude or ChatGPT open all day and wants it to see Salesforce, Jira and the finance system. Both requests arrive in the same week, from the same budget, and they look like the same problem.
They are not. Glean or connect apps to Claude and ChatGPT is a choice between two different jobs. One is a search and discovery layer over your content. The other is live tool access from the assistant people already use. This post sorts out which you are actually buying, and what governance a direct connection needs before you turn it on.
Two terms before we start. MCP is the Model Context Protocol, the standard way an AI client calls tools in other systems. An endpoint is the single address an AI client signs in to.
Glean or connect apps to Claude and ChatGPT: what each path gives you
Glean gives you permissions-aware workplace search and an agent layer across your apps (glean.com/enterprise-search and glean.com/ai-agents, checked October 2026). Elaichi gives Claude, ChatGPT and Cursor live, governed tool access to those apps from one address. Pick by what people need to do, not by which vendor spoke to you first.
| Where connectors come from | How a person signs in | How access is restricted | What the audit record holds | How it is hosted | |
|---|---|---|---|---|---|
| Glean | 275+ native and MCP-based connectors; external MCP servers proxied through the Glean MCP Gateway | Each person uses their own Glean access on the tenant-specific Remote MCP Server | Permissions enforced for every action an agent takes; admins can disable MCP or hide the end-user Configurator | Not on the pages we checked | Not on the pages we checked |
| Elaichi | Connectors Elaichi authors and runs, plus native MCP connectors (vendors' own servers); a team can add its own remote MCP server | Each member connects their client once and approves their own OAuth grant at one address | Restrictions on a role or one member, per connector or per tool; grant scopes and chosen toolboxes | Person, tool, connector, connection, MCP client, how approval was given, what it touched, outcome | Hosted by Elaichi, one endpoint for every organization |
The Glean cells come from Glean's own pages: enterprise search, Agents, Connectors, Remote MCP Server, About Glean MCP server and agent governance, checked October 2026.
Glean's own enterprise search page calls Glean Search "AI-powered workplace search". It also says "Results are real-time and permissions-aware, so everyone sees only what they should" (glean.com/enterprise-search, checked October 2026). Its Agents page says "Glean's agentic engine lets agents reason through tasks, plan next steps, and take action using enterprise context" (glean.com/ai-agents, checked October 2026). Its Connectors page describes "human-in-the-loop controls that let you require approval before execution" for the actions agents take (glean.com/platform/connectors, checked October 2026).
Glean also publishes its own Remote MCP Server. That page says the server "is tenant-specific and permission-aware. Each person uses their own Glean access". It adds that "Administrators can disable MCP or hide the end-user Configurator" (developers.glean.com/guides/mcp, checked October 2026). So Glean can be reached from an MCP client too, through Glean's own layer.
The direct path is different in kind. Elaichi serves MCP at one endpoint, https://api.elaichi.ai/mcp, the same address for every organization. Claude, ChatGPT and Cursor all sign in there with OAuth. Who is calling comes from the OAuth grant each person approved, not from the URL (one endpoint against one per team). The tools behind it come from a governed connector catalog. Elaichi authors and runs most of those connectors itself. The rest are native MCP connectors: vendors' own MCP servers that Elaichi staff publish and Elaichi governs in front of.
Who each path suits
Glean suits a company whose main complaint is that nobody can find anything. Many content sources, many documents, and people who ask where the Q3 pricing deck is more often than they ask to refund a charge. Its Connectors page says "With 275+ native and MCP-based connectors, Glean taps into your app ecosystem" (glean.com/platform/connectors, checked October 2026). Its agent governance page says "Glean enforces permissions for every action an agent takes" (glean.com/ai-agents/agent-governance, checked October 2026).
A direct connection suits a company whose people already work inside Claude or ChatGPT and need those assistants to act in systems of record. A support lead asking Claude to find both charges for a customer. A rep asking ChatGPT what changed on an account this week. Finance reading Stripe without being able to issue a refund. The work is transactional, not archival, and it lives in a handful of apps each team touches daily.
The honest third case: you may need neither yet. Suppose two people use AI at work, each connecting one vendor's own MCP server to their own client. Buying a layer over that is premature. When you don't need an MCP gateway yet sets out what has to be true before a control plane earns its keep.
What governance a direct connection needs
Three things, and they are not optional once more than a few people are connected.
Per-person sign-in. Every call must act as a specific person, not as a shared bot account. In Elaichi each member connects their own client and approves their own OAuth grant. Every MCP request re-reads that grant and the person's current permissions, so a role change does not wait for the token to expire. Where the app on the other end should see the person, the tools have to run on that person's own connections.
Restrictions. A restriction rule sets which connectors and which individual tools a target may reach. A rule targets a role, applying to everyone who holds it, or one member; there is no organization-wide or team target. Within a layer, allow rules merge into one allowlist, block rules into one blocklist, and a block always beats an allow. An empty allow rule is a trap: the allowlist stage engages on the presence of an allow rule, not its contents, so an allow rule that names nothing denies everything. Role, team and restriction changes take effect within about two minutes.
An audit log. Every connected-tool call that reaches execution writes a row. It names the person, the tool, the connector, the connection it reached, the MCP client, how approval was given and whether it worked. Elaichi keeps 90 days of audit history. For longer, forward it to your own log service. Logging destinations come with the Black plan, which is launching soon, and Datadog is the destination that delivers.
What a direct connection does not give you is per-call approval over MCP. The grant is the approval. The person chose those scopes on Elaichi's own consent screen for that named client, and Elaichi runs every call that passes the scope check as already approved. Whether a client asks before each call is the client's own behavior. For write or modify actions, ChatGPT may ask for confirmation, depending on app permissions and the action's context (help.openai.com, checked October 2026). Claude's connector tool permissions take one of three settings: Always allow, Needs approval or Blocked (support.claude.com, checked October 2026).
Can you run both?
Yes, and for many companies that is the right answer. The two paths do different jobs, and nothing about one forecloses the other.
Glean's admin page "About Glean MCP server" lists "External MCP servers: Third-party MCP servers you proxy securely through Glean" among what a Glean MCP server can expose. Those are "brought in through the Glean MCP Gateway" (docs.glean.com, checked October 2026). On the other side, an organization on Gold can add its own remote MCP server to Elaichi as a connector. That brings the server under the same roles, restrictions and audit log as every other connector.
The practical split: search and discovery over documents on one path, transactional reads and writes in systems of record on the other. Budget for the second only when a named team has a named job that the first cannot do.
Where a direct connection goes wrong
Four failure modes, all avoidable.
The first is a shared connection nobody meant to share. Every call through a connection uses that connection's own credential, whoever is calling. A connection shared at use therefore runs every caller's call as its owner. That is sometimes what you want and sometimes a quiet escalation. AI agents with employee permissions covers the difference.
The second is scope confusion. "Run your connected tools" lets a client run the person's connected tools in connected apps, writes and deletes included. "Delete data and remove access" covers Elaichi's own deletes, not the app's. No consent checkbox holds a client to reading a connected app. To do that, pick toolboxes of read tools at consent, or block the write and delete tools with a restriction.
The third is an allow rule written for one app. Holding a role to one app's read tools switches that role to an allowlist. It then needs allow rules for every other app it uses, or everyone in it loses those apps (choosing an allowlist or a blocklist).
The fourth is offboarding. Removing or suspending a member revokes their grants and stops their clients on the next call. It does not remove their accounts in the connected apps. Offboarding when the agent holds access walks the rest.
How to decide this week
Write down the five questions people most want answered, and mark each one find or do. If most are find, a search layer is your first purchase. If most are do, and they name two or three apps a team already lives in, connect directly. Put the three controls in place before anyone else joins.
If you are taking the direct path, the first-week setup checklist gives the order. The connector catalog tells you whether the apps on your list are there, either authored by Elaichi or carried as the vendor's own MCP server.
