Glean vs Unblocked for engineering teams: why not just use Glean?

If you already own Glean and the need is general knowledge search, use Glean. The case for Unblocked starts when engineers and coding agents ask questions that span a pull request, an incident, a Slack thread and the code. Those sources need settling before they reach the agent. A context engine is a service that ingests code history, PRs, tickets, chat and docs, settles conflicts between them, applies the asking user's permissions, and returns one cited answer. Enterprise search is a product that indexes every app a company uses and returns ranked, permission-filtered matches. Glean is enterprise search and Unblocked is a context engine. That is the whole difference.

What Glean does well, and when to stop reading here#

Glean is the better fit for company-wide search, and it says so. Its site lists "275+ out-of-the-box connectors across enterprise apps and ecosystems" (Glean connectors). The categories span HR, Sales & Marketing, Support, Learning & Enablement and Engineering & Analytics. The home page names the audience: "From engineering to sales to support to HR — Glean helps every department move faster with trusted AI" (Glean). The connectors page adds: "All data permissions are inherited and strictly enforced, so users only see what they're allowed to."

Glean's engineering coverage is real, too. The GitHub connector indexes pull requests, reviews, comments, diffs, commits and issues. Incremental crawls "run every 10 minutes for additional reliability beyond webhooks" (Glean docs). The engineering page promises to "pull together related tickets, incidents, code changes, logs, and prior discussions" (Glean for engineering).

So stop reading here if three things are true. You own Glean already. Most engineering questions are about finding things, such as where the runbook is, who owns a service, or which doc covers the deploy. And your coding agents are not yet asking questions of their own. For them a second tool is cost without return.

What each MCP server hands a coding agent#

The difference shows up on the first tool call. Glean's MCP server offers search ("search across your entire knowledge base with permission-aware results"), chat, read_document, code_search, employee_search and knowledge_graph_query, among others (Glean docs). It serves 22 listed hosts, including Claude Code, Cursor and Codex, under usage-based FlexCredit pricing (Glean docs). A search call returns ranked, permission-filtered matches, and chat hands the question to Glean's assistant. Sorting matches is the agent's job.

Unblocked's MCP server exposes two tools. context_research runs "multi-source research across code, PRs, docs, issues, and messaging" and "returns a synthesized answer with citations". context_get_urls fetches the content behind a URL the agent meets along the way (Unblocked docs). The same server serves Claude Code, Cursor, Codex, VS Code, JetBrains and Windsurf.

The payload matters because the agent keeps paying for it. Anthropic's guide says that when an agent calls tools, "the results become part of the conversation history and count against the context budget on every subsequent turn" (Claude Cookbook, March 2026). A March 2026 paper opens from the same finding: LLMs show "significant performance degradation as context length increases" (Cao et al., 2026). Claude Code warns when a tool result passes 10,000 tokens and caps it at 25,000 by default (Claude Code docs). We measured the gap on one production repository, same prompt, model and turn budget, with and without Unblocked attached: 42 percent fewer tokens and 64 percent fewer tool calls. The method is in same prompt, same model, different context. The full payload comparison is in Glean MCP vs a context engine.

Conflict resolution: what Glean does not claim#

The best-matching document rarely answers an engineering question. Consider a made-up case: a design doc says 15-minute token expiry, the merged code says 30, a PR explains the change, and an incident thread holds the reason. The agent needs to know which one wins today. Unblocked ranks sources "using recency, authority, the expert graph" and proximity to the asker's own work. Code is treated as "the strongest ground truth for how the system behaves right now". Conflicts the engine cannot settle are shown, so the person or agent can decide (inside the Unblocked context engine). Glean's docs describe permission-aware search and a knowledge graph query tool. In the Glean pages we read, we found no statement on which source wins when a design doc and the merged code disagree. We treat it as absent rather than guess.

Identity: who is asking shapes the answer#

Identity-scoped delivery is when the system uses who is asking to shape what it fetches and what the answer says. Every request to Unblocked carries who is asking, so retrieval is filtered "down to what the asking identity is authorized to see". The search is then narrowed by that engineer's repos, past work and teammates. Data Shield maps each user's "existing permission levels across the connected data sources" and filters answers to data they may view (Data Shield). Glean enforces permissions as well. Unblocked also uses the identity to decide what the answer should contain.

The sources differ in kind. Unblocked's 53 integrations are the systems engineering runs on (Unblocked integrations). The list has GitHub, GitLab, Bitbucket, Azure DevOps, Jira, Linear, Slack, Teams, Confluence, Notion, Datadog, Sentry and incident.io, plus CI from GitHub Actions to Buildkite. There is no HR system or CRM on the list, on purpose. Youssef Eladawy, Senior Software Engineer at HeyJobs, describes the difference between searching and asking: "I keep Unblocked globally installed so it's in every Claude session by default. During the axios incident, I didn't have all of HeyJobs' repos on my laptop, so I asked Unblocked to find every repository we had that could be vulnerable and report back. It came back with repositories I wasn't fully aware of. That's the pattern — I know what I'm looking for, but Unblocked finds what I don't know about."

How to decide this week#

Run both against your own questions before buying anything. The 2026 Stack Overflow survey found that 93 percent of developers need source attribution to trust AI output. It also found Claude Code (66 percent) and GitHub Copilot (59 percent) are the leading agents (Stack Overflow). The host is rarely the constraint. The payload is.

• Pick five questions where the answer changed last quarter and spans a PR, a ticket and a chat thread. Ask each through Glean's MCP server and through Unblocked from the same agent.
• Check whether the answer names the current artifact, says which source governs, and cites enough to verify in a minute.
• Change a permission at the source and ask again as that user.
• Count tokens and tool calls per task.

If Glean clears all four on your questions, keep it and skip the second tool. If the agent is left sorting matches, add Unblocked for engineering and keep Glean for everything else.