Guide: Search and curate trusted knowledge in flow with the Stack Internal MCP server

Overview

What is MCP?

Model Context Protocol (MCP) is an emerging standard that enables AI agents to connect securely to external applications. An MCP server provides building blocks—called prompts, tools, and resources—for adding context to your AI:

• Prompts: guide agents in natural language (e.g., “/search” or “/create-question”).
• Tools: structured actions (search, get_article, create_question, etc.).
• Resources: represent the underlying data (questions, answers, articles, comments, tags).

What makes the Stack Internal MCP server different?

The Stack Internal MCP server securely exposes your company’s trusted, human-validated Q&A, articles, and documentation to your IDEs and AI applications in real-time. This gives your developers and employees AI support that is:

• Bidirectional: retrieve and create content using natural language in IDEs and AI applications.
• Accurate: responses are grounded in your organization’s vetted knowledge.
• Context-aware: answers adapt to your enterprise’s standards, tools, and processes.
• Attributable: responses are cited back to their original source, adding trust.
• Secure: uses OAuth 2.0 and runs in your infrastructure for full privacy and compliance.

MCP server set up

This guide assumes you have already set up the Stack Internal MCP server. For guidance on how to set it up with more examples of prompts and tools available to use, please refer to this quickstart guide.

Supported MCP clients

The Stack Internal MCP server supports most MCP-compatible clients, like:

• OpenAI ChatGPT
• Claude
• Google Gemini CLI
• Amazon Quick Suite
• Visual Studio Code
• Cursor
• GitHub Copilot
• Lovable.dev
• Microsoft tools

Engineering use cases

Auto-generate internal documentation

Knowledge capture throughout the software development lifecycle is critical to team handoffs, understanding historical decisions, and ensuring your copilots and agents don’t hallucinate. Engineers should store company-specific knowledge for topics like API key management, tool support, and agent orchestration in Stack Internal so the content is structured, verified, and trustworthy.

Example prompts

With this prompt you can reference Stack Internal to create project-specific documentation that meets company standards.

I need to create a README file for the microservices project:

1. search Stack Internal for the most recent README standard
2. analyze the microservices project's architecture
3. create a README formatted in markdown

This prompt can be used to create structured Stack Internal knowledge from a commit so it’s verified and discoverable.

Retrieve my latest github commit and draft a Stack Internal Q&A on how we handle caching logic

Outcomes

• Capture institutional knowledge.
• Improve onboarding and handoffs.
• Reduce silos and repeat work.

Onboard engineers to a new project or area

Joining a new project or code area comes with a significant learning curve and cognitive load, especially when learning about legacy systems and processes. The Stack Internal MCP server helps shorten time-to-proficiency by allowing engineers to access authoritative, context-rich knowledge directly in their IDEs.

Example prompt

Use this prompt to search vetted Stack Internal knowledge to better understand enterprise architecture, release history, code ownership, and more.

I just joined a gen ai team building an application that will run on microservices. Before I start building and writing any python code, research the following on Stack Internal:

1. What tools we use for microservices
2. Our company's best practices
3. The SMEs worth connecting with for this project
4. Anything else that is relevant for planning the architecture

If you find any gaps in the documentation, call that out explicitly.

Outcomes

• Ramp up engineers in less time.
• Improve team morale and productivity.
• Ship code to production faster.

Troubleshoot an error message

Encountering an unknown error can lead to the tedious process of switching tools, searching available resources, and trying to find the right experts who know the solution. Stack Internal's metadata and trust signals allow for more relevant AI responses via the MCP server so troubleshooting is less frustrating.

Example prompt

This prompt searches for company-specific error messages, processes, and domain experts when stuck on an error message so you can implement a fix and move on.

I'm hitting an error "TOKEN_BUDGET_POLICY_MISMATCH" - can you search Stack Internal to see if there's a documented fix for this? I'd also like to know who the relevant SMEs are for AI-related engineering projects so I can ask them for help if needed.

Outcomes

• Achieve faster resolution times.
• Ensure fixes are compliant.
• Reduce your bug backlog.

Scale veteran engineer knowledge

Scaling veteran engineer knowledge in Stack Internal reduces repeat questions during onboarding and gets junior engineers up to speed with minimal disruptions.

Example prompt

This prompt finds questions without an accepted answer for a specific tag so SMEs can find and resolve ones in their areas of expertise.

Using the #search tool, find the top 5 questions without an accepted answer for the gen-ai tag. 

Outcomes

• Preserve institutional knowledge.
• Improve onboarding and handoffs.
• Protect veteran engineer time.

Support use cases

Proactively fill knowledge gaps

Deflecting support tickets can be a challenge if you are unaware of unique, technically complex product scenarios buried in comments and support tickets. With the Stack Internal MCP server you can quickly discover and convert knowledge gaps into reusable Q&A and documentation.

Example prompt

This prompt helps you identify and document these product edge cases in Stack Internal so the knowledge is structured, validated, and accessible by employees.

Search comments on posts in Stack Internal related to how we handle caching logic and identify knowledge gaps that could be converted into new question and answer pairs

Outcomes

• Reduce mean time to resolution (MTTR).
• Enable more continuous coverage.
• Lower ticket escalation rates.

Build a self-service knowledge base

When employees can troubleshoot issues on their own it prevents ticket escalations and frees up your support team to provide an exceptional customer experience. You can use both the Stack Internal MCP server and an MCP server for your ticketing system to convert fragmented information into knowledge that's continuously discoverable and updated in Stack Internal.

Example prompt

Build a self-serve experience with this prompt that creates structured, reusable knowledge from information buried in support tickets.

Find all tickets closed in the last 30 days related to local dev environments breaking due to the 26.2 macOS update. Draft a how-to guide in Stack Internal on best practices for troubleshooting your Docker environment using our company's style guide.

Outcomes

• Reduce mean time to resolution (MTTR).
• Enable more continuous coverage.
• Lower ticket escalation rates.

Onboard new support agents

Storing your support team’s knowledge in a central repository reinforces best practices, avoids ticket escalations, and gets new support agents up and running faster. The Stack Internal MCP Server makes it easy to retrieve relevant onboarding information using natural language.

Example prompt

This prompt helps a junior support engineer troubleshoot an issue during onboarding using verified Stack Internal documentation.

I'm trying to deploy the API but I'm getting a 403 error. I've looked at the public docs and they don't help. Search Stack Internal for posts about this issue and list employees who might be able to help resolve it.

Outcomes:

• Improve onboarding and handoffs.
• Enable more continuous coverage.
• Reduce silos and repeat work.