Knackly MCP. Assemble legal and business documents with conditional logic.
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Knackly MCP Server generates legal and business documents using smart templates and conditional logic. Your AI client connects to this server to automate document creation from structured templates, handling fields and complex rules for every unique situation.
It lets you manage workspaces, track generation history, and build documents through natural conversation.
What your AI agents can do
Create data record
Adds a new record into a specified data model.
Get record details
Retrieves all data points for one specific record.
List catalogs
Shows all data catalogs available in your workspace.
Creates a document using a template by filling in specific field data, respecting conditional logic rules.
Adds new data entries (create_data_record) or pulls detailed information for a specific record (get_record_details).
Lists available data models, catalogs, and workspaces to see what kind of data you can work with.
Retrieves a log of previously generated documents and the details of the generation process.
Lists configured webhooks and available apps for integration management.
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Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
Knackly MCP Server: 8 Tools for Document and Data Ops
Use these eight tools to manage workspaces, list data models, create records, and automate the generation of complex, conditional business documents.
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Start using Knackly on Vinkius019dd113create data record
Adds a new record into a specified data model.
019dd113get record details
Retrieves all data points for one specific record.
019dd113list catalogs
Shows all data catalogs available in your workspace.
019dd113list data models
Retrieves a list of all data models within a selected catalog.
019dd113list data records
Lists all available records for a given data model.
019dd113list generated documents
Retrieves a list of previously automated documents and their metadata.
019dd113list webhooks
Shows all configured webhooks for automated external notifications.
019dd113list workspaces
Lists all the distinct Knackly workspaces you have access to.
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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 8 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Generating a document shouldn't involve copying fields between five different tabs.
Today, generating a single agreement means opening the master template. You copy the client's name from the CRM tab, paste it into the template. Then you open the legal team's spreadsheet to find the governing law, copy that, and paste it into the document. You repeat this process for every variable field, making it slow and prone to human error.
With the Knackly MCP Server, your agent handles the whole process. You tell it, 'Generate the Service Agreement for Acme Corp.' The server automatically pulls the client name, the governing law, and the effective date, assembling the document perfectly every time. You just get the finished file.
Knackly MCP Server: Generate Documents with Logic
The manual steps that vanish are the opening of the template, the switching between the CRM, the spreadsheet, and the document itself. You don't manually map fields or check if a date is required; the server handles the schema validation and population automatically.
The difference now is that document assembly is a single, automated step. You provide the data, and the server delivers the final, conditional document. It's done.
What you can do with this MCP connector
You connect your AI client to Knackly to build and manage complex legal and business documents. You don't just generate files; you automate the entire process using structured templates and conditional logic. Your agent handles the conversation, letting you build documents by simply talking to the server. You can manage your data sources, track history, and set up external notifications all through the same connection.
Manage Data Records
To start, you can see all the distinct workspaces you've got with list_workspaces. You'll also find list_catalogs to see every data catalog available in your workspace. From there, you can pull a list of all data models using list_data_models within a specific catalog, or check out all the actual records using list_data_records for a data model.
When you need to add new info, you run create_data_record to drop a new record into a specific data model. If you need all the details on one entry, get_record_details pulls every single data point for that record.
Generate Documents from Templates
To create a document, your agent uses a template and fills in the fields with data you provide. The server respects conditional logic, so if a section doesn't apply, it just skips it. This keeps your documents accurate, no matter how unique the situation is. You can manage the entire workflow without ever leaving your AI client.
Track Document History
Need to see what you generated last week? list_generated_documents gives you a list of every previously automated document and all its metadata. You'll also find list_webhooks to show every external notification you've set up, and you can manage those connections through list_webhooks.
019dd113-c616-7071-b814-37298fa455af 
How Knackly MCP Works
- 1 Subscribe to the Knackly server and provide your API Key.
- 2 Tell your AI client to perform a task (e.g., 'Create an NDA for Acme Corp').
- 3 The server executes the necessary tools (like
create_data_recordorlist_data_models) to gather data, then generates the final document.
The bottom line is, your AI client uses the server to handle the complex steps—from data gathering to document assembly—all in one flow.
Who Is Knackly MCP For?
This is for Legal and Operations teams dealing with high-volume, repetitive paperwork. If you spend time generating contracts, proposals, or internal reports where the content changes based on who the client is or what the agreement covers, this saves you the manual process of opening templates and copying fields.
Generates specific legal documents, like NDAs or Service Agreements, by pulling client names, dates, and governing laws from structured templates.
Automates the creation of internal policy documents or onboarding packages that require pulling data from multiple sources (e.g., employee ID, department, start date).
On-demand generation of proposals or sales agreements, using real-time data inputs to ensure the document is accurate before sending.
What Changes When You Connect
- Automate document creation. Instead of manually filling out templates, your AI client generates final documents by using the
create_data_recordtool to populate required fields and then assembling the final output. - Centralized data management. Use
list_catalogsandlist_data_modelsto see exactly where your structured data lives, so your agent knows what fields are available for the document. - Audit every output. The
list_generated_documentstool gives you a full history, letting you track who created which document and when. No more searching through shared drives. - Streamline data gathering. If you need details on one specific client, the
get_record_detailstool pulls all necessary information in one API call, instead of requiring multiple database queries. - Manage integrations easily. Use
list_webhooksto see all the external connections and apps configured, making sure your document workflow talks to the right systems. - Scope your work.
list_workspaceslets your agent switch context and manage multiple, separate business environments without confusion.
Real-World Use Cases
Drafting an NDA for a new client
A sales specialist needs an NDA. They ask their agent to 'Draft NDA for Acme Corp.' The agent uses list_data_models to identify the required fields (Party A Name, Effective Date). It then uses create_data_record to map the client data, generating the final, correctly formatted PDF.
Creating an HR onboarding packet
An HR manager needs to generate a package for a new employee. They ask their agent to compile the packet. The agent checks list_data_records for the employee's ID and department, and then uses the document generation tools to assemble the final HR packet.
Reviewing a legal document template
A legal team member needs to know what fields are required for a 'Service Agreement' template. They ask their agent to check the schema. The agent runs list_data_models and reports back the specific fields and data types needed before any generation happens.
Tracking document compliance
An operations analyst needs to prove that a specific agreement was generated last month. They ask their agent to check the history. The agent calls list_generated_documents, providing a clear, auditable list of all past document outputs.
The Tradeoffs
Assuming data existence
The agent just tries to generate a document using a random name and assumes the data is there. This fails because it doesn't check if the template has the necessary fields or if the record exists.
→
First, use list_data_models to confirm the structure. Then, use get_record_details to pull the exact data for the required record. Finally, trigger the document generation.
Mixing up scope
Trying to generate a document from a record in Workspace A, but accidentally pulling data from Workspace B. The document gets mixed up and is unusable.
→
Always start by using list_workspaces to confirm the target scope. This keeps your agent focused on the correct environment before accessing data via list_data_records.
Forgetting the workflow steps
The agent gets the template name and immediately tries to generate the document, skipping the necessary step of populating the data first. The output is incomplete.
→
You must use create_data_record or get_record_details to populate the data first. The document generation only works with existing, structured data.
When It Fits, When It Doesn't
Use this if your workflow requires turning structured data into a highly formatted, legally sound document. This is for repetitive tasks like contract generation, proposals, or compliance reports where conditional logic matters. Don't use it if you just need to read a simple list of names or perform a basic lookup; those simple reads are better handled by dedicated data query tools. You do need this when the output must be a formatted, multi-page document that changes based on the input data. If you just need to check connections, use list_webhooks or list_workspaces first.
Common Questions About Knackly MCP
How do I use `create_data_record` with Knackly MCP Server? +
The create_data_record tool lets your agent add a new, structured data entry. You need to specify the data model and provide the key-value pairs for the new record.
What is the difference between `list_data_models` and `list_data_records`? +
list_data_models shows you the structure (the blueprint) of the data, while list_data_records lists the actual instances (the data points) that fit that structure.
Can I track how many documents I generated using `list_generated_documents`? +
Yes. The list_generated_documents tool provides historical data, allowing you to see the total count, which templates were used most often, and the average generation time.
Do I need `list_workspaces` before generating a document? +
It's best practice. Using list_workspaces lets your agent confirm which environment (workspace) the data belongs to, preventing mix-ups between different client accounts or business units.
How does Knackly MCP Server handle conditional content? +
Knackly uses smart templates that contain conditional logic. The server checks the data provided for rules (e.g., 'IF client is 'Enterprise' THEN include Clause X'). The document only includes Clause X if the data supports it.
What is the function of `list_catalogs` when setting up a new document generation project? +
It shows all available catalogs within your workspace. You use this to see the high-level groupings of data you can draw from, helping you decide where to build your template.
If I get an error, how can I use `list_webhooks` to troubleshoot my document workflow? +
This tool lets you see all configured webhooks. Checking these helps you verify if the document generation process is successfully calling external systems or if the connection point is broken.
How can I use `list_data_records` to validate the inputs for a new contract using Knackly MCP? +
It retrieves specific data points from a model. You run this first to confirm the record has the necessary details—like names or dates—before trying to generate the final document.
Can I generate documents from templates through the AI agent? +
Yes. Browse available templates, inspect their required fields, and generate documents by providing field values. Output documents are created automatically.
Can I browse available apps and templates? +
Yes. List all Knackly apps and their associated templates with field configurations, data types, and validation rules.
What API endpoint does Knackly use? +
Knackly uses Bearer authentication against api.knackly.io/v1.
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