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MCP for Support

Overview

Support teams use Glean's MCP server to quickly find solutions, understand ticket context, and deliver accurate responses by accessing the full enterprise knowledge base.

Prerequisites

Recommended connectors:

  • Zendesk or ServiceNow (for ticketing)
  • Slack
  • Confluence or Notion (for internal knowledge bases)
  • GitHub (for technical issues)
  • Jira (for bug tracking)
  • Google Drive (for documentation)

Supported MCP hosts:

  • Claude Desktop
  • ChatGPT
  • Any MCP-compatible interface
  • Embedded in support workflows

Use Cases

1. Ticket Triage and Next Steps

Get immediate recommendations for how to handle incoming tickets based on past resolutions.

What it does:

  • Analyzes ticket content and metadata
  • Finds similar historical tickets
  • Checks for known bugs or ongoing incidents
  • Recommends resolution path and priority
  • Suggests escalation if confidence is low

2. Find Similar Resolved Tickets

Quickly locate past solutions without manual searching.

What it does:

  • Semantic search across ticket history
  • Identifies resolution patterns
  • Filters by customer segment or date
  • Shows verified solutions

3. Build Contextual Ticket Timeline

Synthesize full customer context across all systems.

What it does:

  • Aggregates customer interactions across systems
  • Creates chronological timeline of issues
  • Identifies recurring themes
  • Surfaces relevant internal discussions

4. Draft Ticket Responses

Generate accurate, context-aware responses based on knowledge base and past solutions.

What it does:

  • Pulls from official documentation
  • Incorporates successful past responses
  • Matches your support tone and style
  • Includes appropriate links and resources

5. Identify Knowledge Base Gaps

Find recurring issues that need better documentation.

What it does:

  • Analyzes ticket patterns
  • Identifies documentation gaps
  • Finds frequently escalated issues
  • Recommends content creation priorities

6. Technical Troubleshooting

Access technical documentation and past solutions for complex issues.

What it does:

  • Searches technical documentation and runbooks
  • Connects to engineering systems (GitHub, Jira)
  • Finds past technical resolutions
  • Provides systematic troubleshooting steps

7. Customer Sentiment Analysis

Understand customer satisfaction trends and identify at-risk accounts.

What it does:

  • Aggregates feedback across channels
  • Identifies sentiment trends
  • Flags at-risk customers
  • Surfaces recurring pain points

8. Onboarding New Support Agents

Help new team members ramp up quickly with instant access to tribal knowledge.

What it does:

  • Locates onboarding documentation
  • Finds example tickets and responses
  • Identifies subject matter experts
  • Surfaces process guidelines

9. Bug Verification and Reporting

Determine if a ticket is a bug and gather information for engineering.

What it does:

  • Checks for existing bug reports
  • Finds duplicate reports from other customers
  • Gathers reproduction steps
  • Drafts comprehensive bug reports

10. Feature Request Validation

Understand the scope and priority of feature requests.

What it does:

  • Checks roadmap and planning docs
  • Aggregates feature request volume
  • Assesses business impact
  • Provides informed customer responses

Best Practices

Always Verify Solutions

After Glean suggests a solution, verify it matches current product behavior
before sending to customers. Documentation can be outdated.

Use Specific Ticket References

✅ "Given Zendesk ticket #12345..."
✅ "For customer Acme Corp (SFDC account 001...)..."
❌ "This customer has a problem..." (too vague)

Prioritize Recent Information

Search Glean for solutions from the last 30 days - our product changes
frequently and older solutions may not apply.

Combine Sources

Search both our public help docs and internal troubleshooting guides.
Internal docs often have more detailed technical steps.

Request Confidence Indicators

Include your confidence level and reasoning. If confidence is low, suggest
I escalate to [specific team/person].

Troubleshooting

Getting irrelevant ticket results?

  • Use ticket status filters ("resolved tickets only")
  • Specify date ranges ("in the last 90 days")
  • Add customer segment context ("enterprise customers")

Responses too technical or too vague?

  • Reference your response guidelines: "Use our support tone guide from Confluence"
  • Specify audience: "Draft a response for a non-technical user"
  • Provide examples: "Similar to how we handled ticket #98765"

Missing internal context?

  • Verify Slack channels are indexed (#support, #customer-success, #engineering)
  • Check that internal wikis and runbooks are connected
  • Ensure Jira connector includes relevant projects

Slow response times?

  • Break complex queries into smaller steps
  • Search specific data sources rather than everything
  • Use ticket IDs or error codes for faster lookups

See also