FlowUs MCP. Manage pages, blocks, and databases via chat.
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FlowUs connects your AI agent directly to your entire knowledge base. Your agent can list pages, read blocks of content, and manage structured databases without you ever opening the web interface.
It lets you query multi-dimensional tables and build new entries instantly, treating your workspace like a chat conversation.
What your AI agents can do
Create database row
Adds a new row of structured data to any specified database.
Create page
Creates a brand new page within your FlowUs knowledge base.
Get database
Retrieves the schema definition (the column names and types) for a specific database.
The agent executes complex queries against defined databases, retrieving specific rows based on criteria.
The agent retrieves page metadata and lists all accessible pages in the FlowUs workspace.
The agent lists and reads specific content blocks within a given page.
The agent adds new rows to databases or updates the content of existing pages.
The agent accesses lists of all databases, pages, and users within the FlowUs environment.
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FlowUs MCP Server: 10 Tools for Knowledge Management
These tools allow your AI agent to interact with every part of your knowledge base—from structured databases to unstructured content blocks.
019d843bcreate database row
Adds a new row of structured data to any specified database.
019d843bcreate page
Creates a brand new page within your FlowUs knowledge base.
019d843bget database
Retrieves the schema definition (the column names and types) for a specific database.
019d843bget page
Fetches the full content and metadata for a specific FlowUs page.
019d843blist blocks
Retrieves a list of content blocks, including text and media, from a specific page.
019d843blist databases
Lists every database available in your FlowUs workspace.
019d843blist pages
Lists all the page titles and IDs available in your FlowUs workspace.
019d843blist users
Retrieves a list of all users who have access to the workspace.
019d843bquery database
Runs filtered queries against a database to pull specific rows of data.
019d843bupdate page
Modifies the content and metadata of an existing FlowUs page.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
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Make Your AI Do More
Start with FlowUs, then connect any of our 4,700+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 4,700+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
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- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
What you can do with this MCP connector
FlowUs lets your AI agent talk directly to your whole knowledge base. Your agent can handle complex page organization and database management without you ever touching the web interface. It treats your workspace like a chat conversation. Your agent can list pages, read content blocks, and manage structured databases.
Querying structured data: Your agent runs complex queries against defined databases, pulling specific rows based on criteria using query_database. You can also get the schema definition—the column names and types—for any database with get_database. When you need to add new info, your agent adds a new row to any specified database using create_database_row.
To update existing records, your agent modifies the content and metadata of an existing page using update_page.
Reading and listing pages: Your agent can list every page title and ID in the workspace with list_pages. It fetches the full content and metadata for a specific page using get_page. You can also list all content blocks, including text and media, from a specific page with list_blocks. For a full picture of your setup, your agent lists every database available in the workspace with list_databases, and it pulls a list of every user with access using list_users.
Creating and updating records: Your agent builds new pages in the knowledge base using create_page. It adds new rows of structured data to any specified database with create_database_row. To modify the content of a page, your agent uses update_page.
Your agent can access the full structure of your knowledge base, letting you query databases, list pages, and manage content blocks directly from your chat client.
How FlowUs MCP Works
- 1 Subscribe to the FlowUs server on Vinkius and enter your unique FlowUs API Token.
- 2 Your AI client sends a natural language request (e.g., 'What were the high-priority items on the roadmap?').
- 3 The FlowUs MCP Server maps the request to the appropriate tool (e.g.,
query_database) and executes it, returning the structured data to your client.
The bottom line is, your AI client uses the FlowUs tools to run complex commands against your knowledge base, giving you data without needing to click anything.
Who Is FlowUs MCP For?
Anyone whose job requires synthesizing information from disparate sources—wikis, databases, and documents. This is for the research scientist, the product manager, or the operations team that spends more time gathering data than acting on it. Stop switching tabs; start talking to your knowledge base.
Tracks feature requirements and manages product roadmaps by querying the 'Product Backlog' database.
Creates new documentation pages and updates content blocks across multiple internal wikis.
Oversees shared team wikis and shared databases, running queries on user feedback or process metrics.
What Changes When You Connect
- Database Querying: Stop writing SQL queries manually. Use
query_databaseto ask natural language questions and get the exact data rows you need. - Instant Content Retrieval: Need to know what's on a page?
list_blocksreads the content structure, letting your agent pull out specific text or media information on the fly. - Zero-Click Page Creation: Don't waste time clicking 'New Page'. Use
create_pageto instantly make a new page andupdate_pageto populate it with structure. - Full Workspace Mapping: Get a bird's-eye view of your whole system.
list_pagesandlist_databasesshow you every accessible page and data source immediately. - Team Visibility: Manage who's on the team using
list_users. Your agent pulls the user list so you can track collaboration and participation without going to the user settings page. - Data Integrity: When you make changes, the system handles it. Use
create_database_rowto ensure new data is saved correctly andget_databaseconfirms the schema first.
Real-World Use Cases
Finding the latest product specs
A PM needs to know the current status of the 'Auth Engine' feature. Instead of checking the 'Product Backlog' database, the agent just runs a query. It uses query_database to filter for 'Auth Engine' and 'High' priority items, returning the list directly in the chat.
Updating project documentation
A technical writer finishes a draft and needs to update the main roadmap. The agent uses get_page to check the existing structure, and then runs update_page to replace the old content with the new draft. This avoids manual copy-pasting into the web editor.
Auditing team contributions
The ops team needs to know which departments have access to the shared wiki. The agent runs list_users to get the full roster. It then uses list_pages to check which of those users have access to the 'Finance' page, solving the manual access audit.
Structuring research findings
A researcher gathers notes on a new topic. Instead of manually organizing files, the agent first uses get_database to see the required fields, then uses create_database_row to structure the findings, and finally uses create_page to document the process.
The Tradeoffs
Treating data like files
Trying to find a piece of data by searching page titles and hoping the content is there. This often misses structured metrics or database entries entirely.
→
Always check the database first. Use list_databases to see what structured data exists, then use query_database to pull specific metrics instead of searching vague page text.
Manual content duplication
Copying the same meeting notes from a page into a database entry because the database needs a structured record of the decision.
→
Keep the source of truth in FlowUs. Use get_page to retrieve the full context, and then let the agent format the necessary data points into a row using create_database_row.
Forgetting the schema
Trying to add a new field to a database without checking if the column exists, leading to errors when creating rows.
→
First, run get_database to see the schema. Then, use create_database_row knowing exactly what fields you need to populate.
When It Fits, When It Doesn't
Use this if your workflow requires synthesizing structured data (from databases) and unstructured content (from pages). It's ideal for knowledge workers who need a single source of truth across multiple formats. Don't use it if your need is only to send a message or run a simple script; use a dedicated messaging or code execution tool instead. If you only need to read static content and never write or update anything, simple read-only APIs might suffice, but FlowUs is best because it manages both the reading and the writing of knowledge.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by FlowUs. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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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 server provides 10 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Sifting through scattered wikis and spreadsheets is a time sink.
Today, finding one piece of information means opening the wiki, checking the meeting notes page, then switching to the Jira board, and finally looking up the metrics in the shared Google Sheet. You copy-paste data between four different interfaces, losing context and spending twenty minutes just gathering the facts.
With FlowUs, you simply ask your agent. It runs the necessary tools—like `list_pages` to find the source, and `query_database` to get the numbers—and hands you the synthesized answer in one chat window. The effort drops from hours of clicking to a single conversation.
FlowUs MCP Server: Querying and Managing Data
The manual steps that disappear are the constant switches between the content editor, the database view, and the user directory. You no longer have to manually check if the database schema is up to date or if the page content matches the required format.
The system handles the connections between pages and databases automatically. It treats your entire workspace as one coherent data layer, allowing you to manage information flow without ever leaving your AI client.
Common Questions About FlowUs MCP
How do I use the `query_database` tool with FlowUs? +
You ask your agent a natural language question about the data. The agent translates that into a query for query_database, returning the filtered rows directly. You don't write SQL.
Can I use `list_pages` and `get_page` together? +
Yes. You first use list_pages to get the list of available pages and their IDs. Then, you pass a specific page ID to get_page to pull the full, detailed content for that single page.
What is the difference between `create_page` and `update_page`? +
Use create_page when you are starting a brand new document. Use update_page when the page already exists and you are modifying its content or metadata.
Does FlowUs help with user management using `list_users`? +
Yes. list_users pulls a roster of everyone with access to the workspace. You can then use the agent to manage collaboration by querying who has access to specific resources.
How do I use `create_database_row` and `query_database` together? +
You first use create_database_row to add the necessary data. Then, you use query_database to immediately check the row you just created. This workflow confirms the data was added correctly and lets you work with it right away.
What kind of data can I pass to `list_blocks`? +
You must pass the Page ID to list_blocks. The tool then returns the content blocks, including text and media metadata, so you know exactly what's in the page.
If I want to change content, should I use `update_page` or `get_page`? +
You need to use get_page first to fetch the existing content and structure. Then, use update_page with the new data payload. This prevents you from overwriting necessary sections.
Does `list_databases` tell me what fields are available? +
No, list_databases only returns a list of all available database names. To see the specific fields and schema, you must run get_database using the database name.
How do I find my FlowUs API Token? +
Log in to FlowUs, go to [Settings] → [Integrations] → [Bot Integrations], and create a new integration to generate your API Token.
Can I search for specific data within a database? +
Yes. Use the query_database tool with the database ID. You can optionally provide a JSON filter string to narrow down the results based on your criteria.
What is a 'Block' in FlowUs? +
Like Notion, FlowUs uses a block-based structure. Everything from a paragraph of text to an image or a sub-page is considered a block. You can list these using the list_blocks tool.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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