chain-composition-validator Connector for AI agents.
3 live capabilities
Validate LangChain and LangGraph pipeline integrity
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Why people use chain-composition-validator
Stop LangChain runtime errors with chain-composition-validator
With this MCP, you stop guessing. You can have your agent inspect the entire structure of your LangChain or LangGraph setup. It catches the mismatch between steps before you ever hit run, turning hours of debugging into seconds of validation.
What Vinkius changes
You stop debugging broken LLM pipelines by catching structural errors before they ever run.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 6,100+ Connectors
- Real-world use case 01
Fixing broken LangGraph transitions
An engineer builds a complex state machine, but the agent gets stuck in a loop.
- Real-world use case 02
Debugging schema mismatches
A developer updates a capability's output, breaking the next step in a LangChain.
- Real-world use case 03
Pre-deployment pipeline audits
Before pushing a new agentic workflow to production, a team uses analyze_pipeline_integrity to ensure the entire composition is structurally valid.
Complete set · 3capabilities
The complete chain-composition-validator capability set.
These are the exact actions your AI can choose when you ask it to work with chain-composition-validator.
01—03
3 capabilities in this set.
Part of 3 available through chain-composition-validator.
- 01 Capability
Analyze pipeline integrity
Performs a full structural and logical validation of your entire chain or graph. It catches high-level errors in the composition.
- 02 Capability
Find unreachable nodes
Locates configuration errors where specific steps are defined but cannot be reached in the flow. This prevents dead code in your graphs.
- 03 Capability
Verify schema compatibility
Checks the data contract between two specific connected steps. It ensures the output of one matches the input of the next.
Set up in minutes
One URL. Then ask chain-composition-validator to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use chain-composition-validator from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_5uyF3CnCQcwyNcsVCMHmG3thQpk8EJv0AqMzvQzh/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it chain-composition-validator, and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable chain-composition-validator for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_5uyF3CnCQcwyNcsVCMHmG3thQpk8EJv0AqMzvQzh/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the chain-composition-validator URL.
- Step 03
Save and start
Save the connection and enable chain-composition-validator in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-composition-validator": {
"url": "https://edge.vinkius.com/vk_preview_5uyF3CnCQcwyNcsVCMHmG3thQpk8EJv0AqMzvQzh/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using chain-composition-validator
Open Agent mode in chat and ask: "Using chain-composition-validator, help me...". 3 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-composition-validator": {
"url": "https://edge.vinkius.com/vk_preview_5uyF3CnCQcwyNcsVCMHmG3thQpk8EJv0AqMzvQzh/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using chain-composition-validator
Ask Copilot: "Using chain-composition-validator, help me...". 3 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-composition-validator": {
"url": "https://edge.vinkius.com/vk_preview_5uyF3CnCQcwyNcsVCMHmG3thQpk8EJv0AqMzvQzh/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using chain-composition-validator
Open Cascade and ask: "Using chain-composition-validator, help me...". 3 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"chain-composition-validator": {
"url": "https://edge.vinkius.com/vk_preview_5uyF3CnCQcwyNcsVCMHmG3thQpk8EJv0AqMzvQzh/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using chain-composition-validator
Ask Cline: "Using chain-composition-validator, help me...". 3 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add chain-composition-validator --transport http "https://edge.vinkius.com/vk_preview_5uyF3CnCQcwyNcsVCMHmG3thQpk8EJv0AqMzvQzh/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using chain-composition-validator
Ask Claude: "Using chain-composition-validator, show me...". 3 tools are ready
Where the request belongs
Work chain-composition-validator can move forward.
This is for the engineers building complex, multi-step agentic workflows who are tired of debugging schema mismatches and unreachable nodes in their production graphs.
AI Engineer
Validates the structural integrity of complex LangGraph state machines.
LLM Developer
Ensures data contracts between LangChain steps are strictly followed.
MLOps Engineer
Prevents deployment failures by auditing pipeline logic before rollout.
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Bring your own AI
Change the model, client or framework. Keep chain-composition-validator connected.
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Claude -
ChatGPT -
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Cursor -
VS Code -
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Before you connect
Questions about chain-composition-validator.
The practical details behind the request, access and result.
How can I use chain-composition-validator to fix my LLM workflows?
You can use it to verify that the data passed between different steps in your LangChain or LangGraph setup is correct, preventing the crashes that happen when a required field is missing.
Can chain-composition-validator find errors in LangGraph?
Yes, it specifically looks for structural issues in LangGraph, such as nodes that can never be reached due to incorrect edge logic.
Does chain-composition-validator work with any AI client?
It works with any MCP-compatible client like Claude, Cursor, or Windsurf, allowing your agent to perform these checks directly in your coding environment.
Will chain-composition-validator prevent production crashes?
It helps prevent crashes by catching schema mismatches and logical errors during the development phase, before you deploy your agentic workflows.
How does chain-composition-validator help with debugging?
Instead of manually tracing data through your code, you can ask your agent to validate the connections between steps, which quickly identifies exactly where a data contract is broken.
What does this validator prevent?
It prevents runtime crashes caused by incompatible data passing between connected steps in a LangChain or LangGraph pipeline.
How do I check if my entire graph is connected?
You can use the analyze_pipeline_integrity capability to perform a full structural and logical validation of the entire composition.
Can I validate specific step connections?
Yes, use verify_schema_compatibility to isolate and validate the data contract between two specific connected steps.
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