SketricGen MCP. Run workflows and debug agent steps.
Gemini Works with every AI agent you already use
…and any MCP-compatible client
Just plug in your AI agents and start using Vinkius.
SketricGen connects your AI agents directly to a multi-agent workflow engine. It lets your agent run complex, programmatic workflows; query searchable knowledge bases using vector search; debug execution traces step-by-step; and interact with contact profiles and conversation history.
What your AI agents can do
Check sketricgen status
Verifies the live connection status of the SketricGen server.
Delete conversation
Permanently deletes a specific conversation history record.
Get agent
Retrieves detailed information about a single AI agent component.
Your agent triggers predefined business logic sequences and runs them end-to-end using the run_workflow tool.
The server allows your AI client to list, get details on, and query external knowledge bases for context retrieval.
You pull detailed execution traces via get_trace to see exactly which tools ran, what data was passed, and how many credits were consumed.
The agent accesses CRM-style contact information using the get_contact tool or lists all available contacts with list_contacts.
You list and retrieve details for individual agents (list_agents, get_agent) to manage multi-agent system components.
Ask AI about this MCP
Supported MCP Clients
OAuth 2.0 Compatible Gemini Waiting for input…
SketricGen MCP Server: 18 Tools for Agent Workflows
These tools give your AI client direct API access to execute multi-agent processes, query knowledge sources, and track execution details.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using SketricGen on Vinkius019dd15fcheck sketricgen status
Verifies the live connection status of the SketricGen server.
019dd15fdelete conversation
Permanently deletes a specific conversation history record.
019dd15fget agent
Retrieves detailed information about a single AI agent component.
019dd15fget contact
Fetches the profile details for a specific contact record using identifiers.
019dd15fget conversation
Retrieves the full transcript and context of a defined conversation history.
019dd15fget knowledge base
Pulls details about a specific, searchable knowledge base.
019dd15fget trace credits
Calculates and reports how many resource credits were used by a specific workflow run.
019dd15fget trace
Retrieves the complete sequence and output of a past workflow execution trace.
019dd15fget workflow
Gets the structural definition and parameters of an existing workflow template.
019dd15flist agents
Returns a list summarizing all available AI agents managed by the system.
019dd15flist contacts
Lists all contact profiles that are currently stored and accessible.
019dd15flist conversations
Shows a directory of past conversation records, including their IDs and dates.
019dd15flist knowledge bases
Returns a list of all knowledge bases available for querying and context injection.
019dd15flist templates
Lists various predefined templates that can be used to build new workflows.
019dd15flist traces
Provides a directory of completed execution traces, allowing history review.
019dd15flist workflows
Lists all available workflow templates and their current status.
019dd15frun workflow with contact
Runs a workflow, automatically injecting details from a specific contact profile into the context.
019dd15frun workflow
Executes a specified workflow template using general parameters.
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
- Publish to catalog or keep private
Make Your AI Do More
Start with SketricGen, then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ 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
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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 18 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Manual workflows require jumping between dashboards and logs.
Today, to run a complex process—say, creating an account summary for a new lead—you manually check the CRM dashboard, copy the contact ID, open the knowledge base portal to find policy details, and then paste that into a separate workflow tool. It's clicking through four different systems just to get one answer.
With this MCP server, your agent runs it all in one go. You tell it to run `run_workflow_with_contact`. The system automatically pulls the contact data (`get_contact`), accesses the knowledge base (`get_knowledge_base`), and executes the entire process without you touching a dashboard. It just gives you the final result.
SketricGen MCP Server: Use `run_workflow` to execute complex agent logic.
Before this, running an agent required someone to manually define and initiate every step—'First, call Tool A. Wait for output X. Then use output X in Tool B.' If any step failed or needed a slight parameter tweak, the whole thing ground to a halt.
Now, you just tell your client to run the workflow name. SketricGen handles the state management and execution sequence entirely. It's one single command that runs an entire business process.
What you can do with this MCP connector
Listen up, this server isn't just some chat wrapper; it connects your AI agent straight into a multi-agent workflow engine. You can use your agent to run complex business logic programs end-to-end.
When you need the system running, you kick off predefined sequences using run_workflow, or if the process needs specific customer details, you call run_workflow_with_contact so it automatically injects that profile info into the context. You don't have to guess what steps are available; you can first check out all the blueprints by calling list_templates and then grab the structural definition of any existing workflow using get_workflow.
For managing your agents themselves, you list everything available with list_agents, and if you need deep intel on one specific component, you fetch its details with get_agent.
If the AI needs to talk about customers, it's got access to CRM-style data. You can pull all stored contact profiles using list_contacts or grab the full profile for a known person by calling get_contact.
When context is everything—and it always is—you can manage external knowledge bases. The system lets you list every available corpus with list_knowledge_bases, and then you pull details on any specific one using get_knowledge_base so your agent knows where to find the right facts.
If things go sideways or you gotta prove how a task was completed, you can track it down. You get the full sequence of actions and data exchange from a past run by calling get_trace. This tool shows exactly which underlying tools ran, what inputs were fed into them, and what the final output was.
You also check resource usage—you calculate how many credits were burned on a specific run with get_trace_credits, and you can review history logs by getting a directory of completed execution traces using list_traces.
To keep tabs on conversations or workflows that happened before, you've got full visibility. You list every past conversation record with list_conversations, grab the entire transcript and context for any single chat via get_conversation, and if you need to wipe a mess clean, you permanently delete that history using delete_conversation.
You can also check the server's operational status instantly by calling check_sketricgen_status to make sure everything's connected right. Finally, when you just want a quick look at what templates or workflows exist without running them, you list all available workflow templates using list_workflows, and you get an overview of every stored conversation record with list_conversations.
That’s the whole shebang.
019dd15f-def1-7279-96e3-57675701c271 
How SketricGen MCP Works
- 1 First, subscribe to the server on Vinkius and enter your SketricGen API Key.
- 2 Next, instruct your AI client (e.g., Claude or Cursor) to use a specific tool name, like
run_workflow. - 3 The server executes the workflow, returns the structured output (data/trace), which your agent then processes and presents to you.
The bottom line is: It gives your AI client API access to run complex business logic—not just talk about it.
Who Is SketricGen MCP For?
This is for the AI engineer who needs reliable, visible execution. If you're tired of 'black box' agents that give vague answers without showing their work, this is your fix. You need to track exactly why and how an agent reached a conclusion.
Uses list_workflows and run_workflow to build, test, and debug complex, multi-step automated processes for production.
Leverages tools like get_contact and get_knowledge_base to inject specific, contextual data into agent prompts, ensuring accurate responses.
Monitors tool usage metrics via get_trace_credits and list_traces to calculate costs and optimize resource consumption across multiple agents.
What Changes When You Connect
- See exactly how agents work. Instead of just a final answer,
get_traceshows the full step-by-step execution path, including which tools fired and what data came back for each node. - Stop guessing about costs. Use
get_trace_creditsto check resource usage per run, letting you accurately budget and optimize your agentic systems before they go live. - Context matters. The ability to inject specific contacts using
run_workflow_with_contactmeans the agent has immediate access to relevant CRM data, improving accuracy on first try. - Manage state reliably. Tools like
list_conversationsanddelete_conversationlet your client handle conversation history explicitly—you control what context is available. - Build robust systems with structure. You can list templates (
list_templates) before running a workflow, ensuring you use the correct, validated process every time.
Real-World Use Cases
Support Agent Triage
A user asks about an account issue. Your agent first calls get_contact to pull account details, then uses run_workflow_with_contact to run the 'Tier 2 Support' process. The workflow accesses get_knowledge_base for policy answers and returns a fully contextualized resolution.
Debugging Agent Failure
The agent failed mid-task. Instead of just getting an error message, you call list_traces to find the run ID, then use get_trace to pinpoint which specific tool call (and its inputs) caused the failure. This isolates the bug instantly.
Marketing Campaign Automation
The marketing team needs a report on VIP clients. They ask the agent, which uses list_contacts to pull all 'VIP' records and then calls run_workflow with the compiled list, generating an aggregate report file.
Internal Policy Query
A new employee asks about PTO policy. The agent checks list_knowledge_bases to find the 'HR Policies' base, then runs a query using get_knowledge_base, providing an answer backed by specific section citations.
The Tradeoffs
Treating agents like simple chat bots
Asking the agent to 'figure out what the customer needs' and hoping it just knows. This leads to vague, uncheckable answers with no source data.
→
Instead, define a workflow (using list_workflows or get_workflow) that forces the agent to use get_contact first, then run the process via run_workflow. This makes the data retrieval explicit.
Forgetting context in multi-step tasks
The agent completes Step 1 successfully but starts Step 2 using old data or missing identifiers, causing a cascade failure.
→
Always pass required IDs (like contact UUIDs) directly to the workflow run. Use run_workflow_with_contact to ensure fresh context is available for every step.
Running workflows without logging
You deploy a critical agent and it fails, but you have no record of why or what data was processed before the failure.
→
Use list_traces immediately after running an experiment. This gives you the historical log (get_trace) needed to debug complex failures quickly.
When It Fits, When It Doesn't
Use this server if your agents need more than just natural language processing—if they require structured, multi-step business logic and verifiable state changes. You must use it when: 1) The task requires accessing defined CRM data (get_contact, list_contacts). 2) The outcome depends on complex internal processes (use run_workflow). 3) Debugging agent behavior is critical (rely heavily on the trace tools). Don't use this if your goal is simple Q&A; for that, a basic Retrieval-Augmented Generation setup might suffice. If you only need to list things without running them, general database clients are enough. But if you need state management and execution visibility, SketricGen is necessary.
Common Questions About SketricGen MCP
How do I debug a failed agent task using get_trace? +
You call list_traces to find the run ID, then use get_trace with that ID. This shows every tool called and its specific input/output pair, letting you pinpoint exactly where the process broke.
Does run_workflow_with_contact give me CRM data? +
Yes. It runs a workflow while automatically injecting context from a specified contact profile using get_contact. This ensures the agent's actions are grounded in real-time client data.
What is the difference between list_agents and get_agent? +
list_agents shows you names of all agents available. You use get_agent when you need specific, deep details about one agent's configuration or capabilities.
Can I track how much an agent uses? How is it done with get_trace_credits? +
The get_trace_credits tool calculates the cost associated with a run. This lets you monitor resource usage and optimize your workflows to minimize token or compute consumption.
I need to query external documentation; should I use list_knowledge_bases first? +
Yes. First, use list_knowledge_bases to confirm the correct base name. Then, you can use get_knowledge_base or let your workflow trigger the retrieval process.
How do I test if my API credentials are valid using `check_sketricgen_status`? +
You run check_sketricgen_status. This verifies your connection to the SketricGen platform and confirms your API key is active. It's the first thing you should do before building complex, multi-agent workflows.
If I need to remove a user’s data after a session, how do I use `delete_conversation`? +
Call delete_conversation. This immediately purges the specific chat history from SketricGen's records. It is your tool for maintaining strict conversation privacy and compliance.
Before building a new agent flow, should I use `list_templates` to see existing options? +
Yes, check list_templates. This shows you pre-built workflow blueprints. Using an existing template lets you adapt proven structures instead of starting from scratch.
How can my AI analyze the execution steps of a specific workflow? +
Simply use the debug_workflow_trace tool. Your agent will instantly retrieve the execution trace, detailing every tool call, data transfer between agents, and total credit consumption per run.
Is it possible to programmatically manage and query documents for my agents? +
Yes. By executing the manage_knowledge_base action, your AI agent can upload files (PDF, DOCX, TXT, HTML) or search existing repositories to provide context-aware, highly accurate responses.
Can I automatically trigger workflows tied to specific customer records? +
Absolutely. Ask the agent to use the run_workflow_for_contact tool. It will execute the designated workflow while injecting the specific contact's history, enabling personalized, data-driven outputs.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.