GitScrum Knowledge MCP for AI. Turn scattered chats into structured, searchable knowledge.
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GitScrum Knowledge manages your team's collective memory in one place. It lets you build structured knowledge bases, store meeting decisions as persistent notes, and search across every single discussion thread, wiki page, and task without leaving your AI agent.
What your AI can do
Create channel
Sets up a new, dedicated communication channel for the team.
Get channel
Retrieves detailed information about a specific discussion channel.
List channels
Provides a list view of all active discussion channels in the workspace.
Query tasks, wikis, notes, and discussions in a single search request.
Create, update, and archive structured notes to store context, meeting outcomes, or architecture decisions.
Generate nested wiki pages for formal process guides and technical write-ups.
List channels, read messages in threads, or search specific discussion streams.
View revision histories for both notes and wiki pages to see how information changed over time.
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GitScrum Knowledge: 28 Tools
Use these tools to manage everything from core notes to wiki documentation, allowing your agent to search and organize your entire workspace.
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Start using GitScrum Knowledge on VinkiusCreate Channel
Sets up a new, dedicated communication channel for the team.
Get Channel
Retrieves detailed information about a specific discussion channel.
List Channels
Provides a list view of all active discussion channels in the workspace.
List Discussions
Retrieves a list of every project's discussions available for querying.
Create Note Folder
Creates organizational containers (folders) for grouping related notes by project or...
List Note Folders
Lists the various folders used to categorize and organize agent memory.
Rename Note Folder
Changes the name of an existing note folder for better clarity.
Channel Messages
Retrieves all messages from a specific discussion channel.
Search Channel Messages
Searches for messages within a single specified communication channel.
Send Message
Sends an update or finding to a designated team channel.
Create Note
Generates a new note to store decisions or context, supporting full markdown...
Delete Note
Permanently removes an existing note from the knowledge base.
List Notes
Gets a list of all notes, perfect for reviewing stored context or decisions.
Move Note To Folder
Organizes a specific note by moving it into a designated folder.
Note Revisions
Shows the full history of changes made to a single note, tracking its evolution.
Toggle Note Share
Changes whether a note is visible and accessible to the rest of the team.
Update Note
Appends context or refines an existing note without overwriting its original content.
Reply To Message
Posts a reply directly into an ongoing discussion thread.
Global Search
Searches across all types of resources—tasks, wikis, notes, and discussions—at once.
Thread Replies
Retrieves all replies associated with a single message within a thread.
Create Wiki Page
Builds a new wiki page that can support nested documentation structures.
Delete Wiki Page
Removes a wiki page, ensuring no data is orphaned.
Get Wiki Page
Gets the full content of a wiki page for review.
List Wiki Pages
Retrieves an index of all wiki pages within the project documentation.
Restore Wiki Revision
Reverts a wiki page to a specific, saved version from its past history.
Wiki Revisions
Retrieves the complete revision history for any given wiki page.
Search Wiki
Performs targeted searches specifically across the documentation content of wiki...
Update Wiki Page
Modifies the content of a wiki page with new information.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 28 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
The biggest time sink isn't writing; it's finding out who wrote it or why they changed their mind.
Today, if you need to understand a design choice made six months ago, you open Slack. You search the `#architecture` channel. You scroll through dozens of messages, filtering by dates and keywords. Maybe you find one key thread, but the actual decision—the final, signed-off rationale—was logged in a separate Confluence page that nobody remembers to link back to.
With this MCP, your agent becomes the central knowledge hub. It doesn't just read chat; it searches across structured notes and wiki pages simultaneously. You ask for the 'API gateway decision,' and it returns the linked note containing the formal rationale alongside relevant discussion snippets.
Centralized Knowledge Management with GitScrum Knowledge
You stop having to jump between five different apps just to build a single, coherent report. You no longer need to manually copy the 'decision rationale' from a note into a wiki page and then reference that in a discussion summary.
Your agent now handles all those steps for you. It keeps your memory structured and searchable, making the difference between knowing something happened and actually being able to prove why it happened.
What your AI can actually do with this
Your agent needs more than just the current chat context to be useful. This MCP connects it directly to GitScrum, turning it into a central repository for everything your team has ever decided or documented. You can create structured notes that act as permanent memory, tracking revisions and decisions over time.
Need process documentation? Build and maintain nested wiki pages right in your agent's workflow. When the team needs context, you don't have to copy-paste from Slack into a document; instead, you simply ask your AI client to search across every note, discussion, and wiki page simultaneously. Vinkius hosts this connector so you can connect it once from any compatible AI client, giving your agent full access to the whole catalog of organizational knowledge.
019d8441-558c-7325-8dd9-fc5e8da27c87 Here's how it actually works
The bottom line is that your agent stops just chatting and starts acting as the team's single source of truth.
First, you subscribe to the GitScrum Knowledge MCP in the catalog and enter your required API tokens.
Next, you point your AI agent at this MCP. Your agent now treats all notes, wikis, and channels as structured data sources.
Finally, you ask a question like, 'What did we decide about payment gateway integration last month?' The agent executes the appropriate tools and returns synthesized answers from across all connected knowledge types.
Who is this actually for?
This MCP is for technical leads, principal engineers, and technical writers who spend more time searching for historical context than they do building features. If your knowledge lives in three different places (Slack, Notion, GitHub), you need this.
Needs to pull together process guides from messy chat threads and formal documentation into a clean wiki page.
Manages asynchronous decision-making, ensuring that every key architectural choice is logged permanently, not just discussed in passing.
Requires a reliable way to give their agent persistent memory and structured context outside of the current session's chat window.
What Changes When You Connect
Never lose context again. Instead of just searching chat logs, you can use the global_search tool to pull decisions from notes and wiki pages simultaneously.
Maintain a single source of truth for your processes. Use the ability to create nested wiki pages (create_wiki_page) so documentation always feels like one cohesive knowledge base.
Track how information changes over time. The note_revisions tool lets you see exactly when and why an architecture decision was updated, giving full accountability.
Keep your agent memory organized. You can create dedicated folders (create_note_folder) and use list_notes to review structured context before starting a new task.
Stay coordinated without constant meetings. Use the discussion tools—like listing channels or sending messages—to communicate findings directly where the team needs them.
See it in action
Onboarding a new engineer to legacy systems
A new hire needs to understand why the payment gateway was changed from Stripe to Adyen. Instead of asking five people, they use global_search and instantly find the original notes (list_notes) detailing the decision, the wiki page explaining the process flow, and the relevant channel messages where the final sign-off occurred.
Documenting a complex feature rollout
The team finished implementing OAuth 2.0. A technical writer uses create_wiki_page to build the master documentation, then uses create_note to log all the specific implementation details and decision rationale. They can then share this knowledge by calling toggle_note_share when it's ready for review.
Debugging a historical bug
A senior engineer finds a subtle bug in the billing module. They check wiki_revisions to see if the process documentation changed recently and use search_channel_messages within the #billing-support channel to find the original conversation where that code path was last discussed.
Managing agent context across weeks
An AI developer needs their agent to remember complex internal APIs over several sessions. They use create_note with structured markdown, then regularly call update_note with new findings. This keeps the agent's 'memory' fresh and accurate without requiring human input every time.
The honest tradeoffs
Relying only on chat history
Asking your agent to summarize a decision, but it only pulls from the immediate conversation thread. The critical context—the 'why'—is buried in an old Slack message or meeting note.
Use global_search first. This tool searches notes, wikis, and discussions together, ensuring you pull the authoritative context, not just what was typed last.
Treating all documentation equally
Trying to find a process guide by searching only chat messages (search_channel_messages). The detailed steps are written in formal wiki pages and won't show up.
Use list_wiki_pages first, then use search_wiki for the specific content. This targets your structured documentation instead of relying on casual conversation.
Overwriting decisions accidentally
Using a simple edit function that wipes out old context or revision history when making minor changes.
Always use update_note to append new context, and if you need historical proof, use the dedicated note_revisions tool. Never overwrite; always augment.
When It Fits, When It Doesn't
Use this MCP if your knowledge is spread across structured documentation (wikis), formalized decisions (notes), and team discussions. It excels at providing a unified view of history. Don't use it if you only need to chat with an agent on current tasks; there are simpler, direct-chat tools for that. If you just need to read the latest messages in one specific channel, get_channel is sufficient. But if you need to synthesize 'why was this decided and what are the process steps?', this MCP is required.
Questions you might have
How do I use `global_search` with GitScrum Knowledge? +
Simply ask your agent a natural language question. The tool executes the search across all connected resources—notes, wikis, and discussions—and provides grouped results showing exactly where the information came from.
What is the difference between `create_note` and `create_wiki_page`? +
create_note is for ephemeral but important decisions or meeting summaries that need revision tracking. Use create_wiki_page when you are building formal, long-form documentation with nested sections.
Can I track changes to a note using `note_revisions`? +
Yes, the note_revisions tool tracks every change made to a note. This lets you see exactly what context was added or removed and when it happened, which is critical for audit trails.
How do I find messages from a specific thread using GitScrum Knowledge? +
You can use thread_replies on an existing message ID. This pulls all subsequent replies into one view, keeping the conversation context intact and easy to review.
When I use `list_note_folders`, how do I organize my agent memory by project or topic? +
You create folders to categorize your notes, keeping related context separate. This structure ensures that decisions and meeting notes stay grouped logically, preventing important information from getting mixed up across different projects.
How do I use `toggle_note_share` to control who sees my work? +
This tool lets you manage the visibility of your notes. You can keep sensitive decisions private while publishing key findings only when and where the team is ready for them.
If I overwrite critical documentation, how do I use `restore_wiki_revision`? +
You restore a wiki page to any previous version using this command. It’s your safety net, allowing you to recover accurate project documentation immediately after an accidental edit or loss of context.
How do I find specific information when I use `search_channel_messages`? +
You target the search directly to a single discussion channel. This filters out all the noise from unrelated conversations, getting you straight to the relevant messages and status updates within that thread.
Can my AI agent use notes as persistent memory across sessions? +
Absolutely — this is a core design goal. Use create_note to store decisions, context, or meeting summaries as markdown notes. Organize them into folders with create_note_folder and move_note_to_folder. Use toggle_note_share to publish findings to the team. Every edit is versioned via note_revisions so you can track how knowledge evolves.
Can the agent search across everything in my workspace at once? +
Yes! The global_search tool performs a unified search across tasks, wiki pages, discussions, user stories, sprints, and notes. Results are grouped by resource type, so you instantly see where every mention lives. It's the fastest way to find anything in your workspace.
Can the agent participate in team discussions and reply to threads? +
Yes. Use send_message to post updates to any discussion channel, and reply_to_message for threaded conversations. The agent can also create channels with create_channel, search message history with search_channel_messages, and review thread replies with thread_replies — enabling fully automated status updates and knowledge sharing.
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