Company Knowledge Connect

Company Knowledge — Connect a Knowledge Source

Goal

Register one or more company-internal knowledge sources so AI agents can fetch examples, documentation, and guidelines directly in chat — without needing to leave the IDE.

Examples of what this covers:

• Internal Spring Boot wrapper / platform library docs
• Company-specific framework guidelines and examples
• Internal API catalogs or developer portals
• Architecture decision records hosted on an internal server
• Design system component libraries

Sources can be fetched in two ways:

• Direct URL — HTTP endpoint returning JSON, Markdown, HTML, or plain text
• MCP Server — a company-hosted or npm-based MCP server exposing knowledge as tools

Security Check First

Confirm _superml/config.yml is in .gitignore:

cat {project-root}/.gitignore | grep _superml

If not present: echo "_superml/config.yml" >> {project-root}/.gitignore

⏸️ STOP — confirm before collecting any internal URLs or tokens.

Step 1: How Many Sources?

Ask:

"How many company knowledge sources would you like to register? (you can add more later)"

Then for each source, run Steps 2–4.

Step 2: Name and Describe the Source

Ask:

1. Short name (used in config and skill commands): e.g., spring-platform, design-system, internal-api-catalog
2. Display name: e.g., Company Spring Boot Platform
3. Description (one line — what it contains): e.g., Internal Spring Boot wrapper with custom starters, annotations, and security modules
4. Which personas use this most?
   • all — every persona (default)
   • Specific roles: architect, developer, modernization, product, team_lead

Step 3: Choose Access Method

Ask:

"How is this knowledge source accessed?

1. Direct URL — HTTP endpoint you can call to get docs or examples
2. MCP Server — a company-hosted or npm-based MCP server

Which? (1 or 2)"

Path A: Direct URL

Ask:

1. Base URL: the root endpoint (e.g., https://internal-dev-portal.corp/api/docs)
2. Auth type: None / Bearer token / Basic auth / Custom header
   • If Bearer: ask for token
   • If Basic: ask for username + password
   • If Custom: ask for header name and value
3. Response format: JSON / Markdown / HTML / Plain text
4. Notes for AI (optional): e.g., "Append ?format=markdown to get Markdown output" or "Use the ?component={name} param to fetch specific component docs"

Test the endpoint:

curl -s {auth_flags} "{base_url}" | head -50

Show the first 50 lines and ask: "Does this look right? (y/n)"

⏸️ STOP — confirm before saving.

Save to config:

company_knowledge:
  sources:
    - key: "{short_name}"
      name: "{display_name}"
      description: "{description}"
      personas: [{personas_list}]   # all | architect | developer | etc.
      access:
        type: url
        base_url: "{base_url}"
        auth:
          type: "{none | bearer | basic | header}"
          token: "{token_if_bearer}"           # omit if not bearer
          username: "{username_if_basic}"      # omit if not basic
          password: "{password_if_basic}"      # omit if not basic
          header_name: "{name_if_custom}"      # omit if not custom
          header_value: "{value_if_custom}"    # omit if not custom
        response_format: "{json | markdown | html | text}"
        notes: "{ai_usage_notes_if_any}"

Path B: MCP Server

Ask:

1. MCP server type:

   • npm_package — runs locally via npx
   • url — hosted MCP endpoint (HTTP or SSE)
   • command — arbitrary shell command that speaks MCP

2. Server name (key in .vscode/mcp.json): e.g., company-docs

If npm_package:

Ask:

• Package name: e.g., @corp/knowledge-mcp-server
• Any env vars needed (name + value pairs)

Add to .vscode/mcp.json:

{
  "servers": {
    "{server_name}": {
      "command": "npx",
      "args": ["-y", "{package_name}"],
      "env": {
        "{ENV_VAR_NAME}": "{env_var_value}"
      }
    }
  }
}

If url:

Ask:

• MCP endpoint URL
• Auth header (if any)

Add to .vscode/mcp.json:

{
  "servers": {
    "{server_name}": {
      "url": "{mcp_url}",
      "headers": {
        "{auth_header}": "{auth_value}"
      }
    }
  }
}

If command:

Ask:

• Full command string (e.g., /usr/local/bin/corp-mcp-server)
• Any args
• Any env vars

⚠️ Security: .vscode/mcp.json may contain credentials:

echo ".vscode/mcp.json" >> {project-root}/.gitignore

Test the MCP server:

"In a Copilot chat, type: @{server_name} help or ask it to describe available tools. Confirm it responds with tool descriptions."

⏸️ STOP — confirm test passed.

Save to config:

company_knowledge:
  sources:
    - key: "{short_name}"
      name: "{display_name}"
      description: "{description}"
      personas: [{personas_list}]
      access:
        type: mcp
        server_name: "{server_name}"   # matches key in .vscode/mcp.json
        notes: "{any_usage_notes}"

Step 4: Add More Sources?

Ask: "Would you like to add another knowledge source? (y/n)"

If yes, repeat Steps 2–4 and append to company_knowledge.sources[].

Step 5: Confirm and Summary

Show all registered sources:

✅ Company Knowledge sources registered:

  1. {display_name}  [{short_name}]
     Access: {url | mcp}  — {base_url or server_name}
     Personas: {all | list}
     Description: {description}

  2. ...

Use `/company-knowledge-fetch` to pull a source into your AI context.

Remind the user to run npx @supermldev/smart-sdlc init again if they want these sources available in the /sml-help skill index, or simply add the source config entries to _superml/config.yml manually.