MCPipe Toolchain

MCPipe Toolchain exposes company knowledge to AI clients through read-only tools for Confluence, GitHub, Jira, Figma, Outlook, Teams, and browser automation. It is designed for safe discovery, retrieval, and summarization of internal information without modifying source systems.

What these servers are

The MCP server is a local bridge between AI clients (like VS Code Copilot) and enterprise systems. It provides a consistent tool surface across:

• Confluence for documentation search and page retrieval
• GitHub for repository discovery, code search, and file access
• Jira for issue lookup, sprint visibility, and project metadata
• Figma for file metadata, nodes, comments, components, styles, and exports
• Microsoft 365 Outlook and Teams for read-only messaging and calendar context
• Browser automation for localhost tab interaction, diagnostics, and React inspection

An optional Electron desktop app hosts provider configuration and starts the MCP HTTP endpoint.

Tools available

Unified read-only tools

• search to query Jira and Confluence in one call
• fetch to retrieve a Jira issue or Confluence page by resource URI

Confluence tools

• search_confluence for keyword search across pages
• get_confluence_page to fetch a page by ID (with chunking)
• search_confluence_cql for advanced CQL queries
• get_confluence_spaces to list accessible spaces
• get_confluence_page_children to navigate page trees
• get_confluence_page_labels to list labels/tags
• get_confluence_page_attachments to list attachments
• get_confluence_page_by_title to fetch a page by title in a space
• get_confluence_page_comments to fetch comments
• get_confluence_page_inline_comments to fetch inline comments
• get_confluence_page_footer_comments to fetch footer comments
• get_confluence_comment_children to fetch reply (child) comments
• get_confluence_page_descendants to fetch descendant pages
• get_confluence_pages_in_space to list pages in a space

GitHub tools

• search_github_code for organization-wide code search
• get_github_file to fetch file content (with optional line range)
• search_github_paths to discover file or directory paths inside a repo
• list_github_repo_tree to browse remote repo structure without a local clone
• list_github_repos to page through repositories
• search_github_repos to find repos by name/topic/language
• get_github_pull_requests and get_pull_request_details
• get_github_issues for issue lists
• get_github_commits and get_github_commit for history and diffs
• get_github_branches and get_github_repo_info

Jira tools

• search_jira_issues for text search
• search_jira_jql for advanced JQL queries
• get_jira_issue, get_jira_issue_basic, get_jira_issue_common, get_jira_issue_full
• get_jira_projects, get_jira_issue_types, get_jira_statuses
• get_jira_issue_comments, get_jira_issue_changelog
• get_jira_issue_transitions, get_jira_issue_watchers
• get_jira_issue_remote_links to list remote links
• get_jira_issue_type_meta_with_fields to fetch issue field metadata
• get_jira_project_issue_types_metadata to fetch project issue type metadata
• atlassian_user_info to fetch the current user
• lookup_jira_account_id to search for user account IDs
• get_jira_boards, get_jira_sprints, get_jira_sprint_details
• get_jira_sprint_issues, get_jira_sprint_report
• get_jira_epic_stories, get_jira_issue_children

Figma tools

• get_figma_file, get_figma_node
• get_figma_comments, get_figma_versions
• get_figma_components, get_figma_component, get_figma_styles
• get_figma_dev_resources, get_figma_images
• get_figma_projects, get_figma_project_files
• get_figma_team_components, get_figma_team_styles

Microsoft 365 tools

• Outlook: search_outlook_emails, get_outlook_email, get_outlook_calendar_events
• Teams: get_teams_joined_teams, get_teams_channels, get_teams_channel_messages
• Teams: search_teams_messages, get_teams_chats, get_teams_chat_messages

Browser and React tools

• Browser control and diagnostics: browser_list_tabs, browser_attach_tab, browser_navigate, browser_wait, browser_wait_for
• Browser inspection: browser_get_dom, browser_get_page_state, browser_get_text, browser_get_element_value, browser_get_count
• Browser visuals and reports: browser_screenshot, browser_full_page_screenshot, browser_generate_report
• Browser styling/debug: browser_get_computed_styles, browser_get_box_model, browser_get_console, browser_get_network_failures
• Browser interactions: browser_click, browser_click_text, browser_click_role, browser_type, browser_set_value
• JavaScript fallback and usage helper: browser_run_script, browser_get_tool_usage
• Browser orchestration checks: browser_compare_figma_styles, browser_verify_user_intent
• React inspection: browser_find_by_component_name, browser_find_by_label, browser_get_component_props, browser_get_component_state, browser_detect_hydration_issues, browser_get_diagnostics

Browser backend modes

Browser tools can run with one of three backends:

• extension (default): current Browser MCP Extension bridge
• playwright: managed Playwright Chromium session (no extension)
• cdp: attach Playwright to an existing Chrome via CDP (no extension)

Configure in config.json (or with env vars):

• browserBackend: extension | playwright | cdp
• browserHeadless: true/false (Playwright mode)
• browserChannel: optional browser channel for Playwright launch
• browserCdpUrl: CDP endpoint URL (default http://127.0.0.1:9222)

Environment variable overrides:

• BROWSER_BACKEND
• BROWSER_HEADLESS
• BROWSER_CHANNEL
• BROWSER_CDP_URL

Notes:

• Localhost-only navigation is enforced (localhost and 127.0.0.1).
• React helper diagnostics remain extension-only in this version.

How our toolchain compares to out-of-the-box MCP servers

Vendors ship their own MCP servers — the official Atlassian Remote MCP, the Datadog MCP, and the Figma Dev Mode MCP. They work, but each was built to expose one product. This toolchain is built for how engineers and AI clients actually work: across systems, safely, and with a tool surface that doesn't drown the model in schemas.

Three design choices make ours better and more flexible than the stock servers:

1. One intent-first tool per provider, not dozens of schemas. Each provider exposes a single tool (jira, confluence, github, datadog, figma, m365, browser) that takes a free-text intent. The tool internally routes to the right operation using semantic search over handler descriptions, then fills parameters with per-system extractors. The model sees ~7 tools instead of ~120, so context stays small and tool selection stays accurate. Stock servers register every operation as its own tool schema, all of which load into context every session and compete during selection (the classic getIssue vs getIssueFull ambiguity).
2. Read-only by design. We deliberately omit write operations even where the upstream APIs and stock servers offer them. This keeps AI workflows safe (no accidental edits), aligned with least-privilege and audit requirements, and deterministic — outputs are reviewable before anything is shared or acted on. Write workflows stay with dedicated, human-driven tools outside the MCP layer.
3. Local bridge, OS-keychain secrets, cross-system reach. The server runs locally and stores credentials in the OS keychain (not env-var tokens or a cloud-hosted OAuth broker). A unified search/fetch spans Jira and Confluence in one call, and browser tools can verify a live UI against a Figma design — orchestration the single-product stock servers can't do.

Atlassian: out-of-the-box MCP vs. ours

	Official Atlassian MCP	This toolchain
Surface area	One tool per operation (~25+ Jira/Confluence tools all in context)	Two intent tools — jira and confluence — plus unified search/fetch
Write access	Creates/edits/transitions issues, creates/updates pages and comments	Read-only by design — no accidental edits from AI
Routing	Model must pick the exact tool and field shape	Free-text intent → semantic routing + extractors pick the operation
Hosting / auth	Cloud-hosted, OAuth broker	Local bridge, OS-keychain credentials
Cross-system	Atlassian only	search/fetch join Jira + Confluence; composes with GitHub, M365, browser

Why ours is better and more flexible: investigation and reporting are the common AI tasks against Atlassian, and those are read-only. We give the same data precision (issue details, metadata, comments, sprint/epic structure, full Confluence content with automatic chunking for large pages) without exposing destructive write paths, and we collapse the tool sprawl into intents the model can hit reliably. If you genuinely need to author content, use the Atlassian APIs or the official MCP for that step — keep AI-driven discovery here.

Datadog: out-of-the-box MCP vs. ours

	Official Datadog MCP	This toolchain
Surface area	Separate tools per data type (metrics, logs, monitors, dashboards, incidents, …)	One datadog intent tool routing to logs/metrics/monitors/dashboards/scopes
Coverage of new endpoints	Limited to the operations the server exposes today	Generic read proxy: query_datadog_api_get and search_datadog_api_post reach any allowlisted read endpoint
Safety	Capabilities depend on the granted API/app keys	Read-only scopes enforced; get_datadog_supported_read_scopes makes the boundary explicit
Auth	Vendor-defined	API + app key in the OS keychain, region/site configurable

Why ours is better and more flexible: incident triage is the real workload — "which monitors are alerting, show me the error logs, plot this metric." Our intent tool covers that directly, and the generic read proxy means you are never blocked waiting for us to add a dedicated tool for a Datadog endpoint: if it's a read path under /api/, you can query it now. The read-only scope guarantee keeps it safe to point an AI agent at production observability.

Figma: out-of-the-box MCP vs. ours

	Official Figma (Dev Mode) MCP	This toolchain
Runtime requirement	Needs the Figma desktop app running locally with a node selected	Talks to the Figma REST API — no desktop app, works headless/CI
Focus	Design-to-code for the current selection; can create/edit designs	Read-only retrieval of files, nodes, comments, versions, components, styles, dev resources, images, design specs, and page screenshots
Surface area	Multiple discrete tools	One figma intent tool; paste a Figma URL and describe what you need
Unique capability	—	browser_compare_figma_styles checks a live localhost UI against the Figma design; browser_verify_user_intent validates implemented vs. intended

Why ours is better and more flexible: the stock Figma MCP is tied to the desktop app and oriented toward generating designs, which makes it awkward for automated review and CI. Ours pulls design context over the REST API from anywhere, and — uniquely — bridges into the browser toolset so an agent can compare what shipped in the running app against the source-of-truth design. That design-vs-implementation check is the high-value workflow for engineers, and no single-product server offers it.

Why the GitHub tools are better for day-to-day usage

GitHub is the system of record for implementation details, code history, and reviews. The GitHub toolset is modeled around common engineering tasks:

• Find usage examples or patterns with search_github_code
• Open exact files and line ranges with get_github_file
• Check current activity with get_github_pull_requests, get_github_issues, and get_github_commits
• Trace change context with get_github_commit and PR detail views

This makes GitHub tools the fastest path from question to verified source, which is essential for day-to-day development, debugging, and review work.

Requirements

• Node.js 18+
• npm

Install

npm install

Build

npm run build

Run

npm run start

Development

npm run dev

Tests

npm test

Browser Automation Guidance

For browser MCP reliability and roadmap details, see:

• docs/browser-agent-playbook.md for deterministic agent operation patterns.
• docs/browser-tools-rfc.md for proposed tool additions and response-contract improvements.

MCP smoke test

To verify a running HTTP MCP endpoint without hand-crafting SSE/session headers:

npm run smoke:mcp

Optional arguments:

npm run smoke:mcp -- --url http://127.0.0.1:6130/mcp

Packaging

npm run package

Install MCPipe Toolchain

Use this guide to install the tool from the GitHub Actions artifact, then configure GitHub authentication.

1. Download the correct artifact (by OS) Open this workflow run:

https://github.com/mcpipe/mcpipe-toolchain/actions/runs/22774217374

From Artifacts, download the package matching your OS:

Windows: dist-app-windows-latest

macOS: dist-app-macos-latest (name may vary slightly)

Linux: dist-app-linux-latest (name may vary slightly)

2. Extract the artifact Extract all files from the downloaded artifact to a local folder.

3. Run the application Windows: macOS: Open the extracted .app, .dmg, or .pkg and complete installation. Linux: Run the extracted binary/AppImage (make executable first if needed). One should see Icon of MCP Tool chain and start the server

<img width="239" height="228" alt="image" src="https://github.com/user-attachments/assets/2782db09-d45b-47e2-9dcf-790d1a3b51fd" />

<img width="1260" height="792" alt="image" src="https://github.com/user-attachments/assets/239fd767-f9ca-4369-928c-6a3268649b66" />

5. Create a GitHub Fine-Grained Personal Access Token Go to GitHub: Profile -> Settings -> Developer settings Open Personal access tokens -> Fine-grained tokens Click Generate new token Set required repository access/permissions for MCP tool usage Generate the token and copy it

6. Configure token in MCP Toolchain Open the MCP Toolchain application Paste the copied token into the GitHub token field in the UI Save/apply settings