Make
MCP ServerFree** - Turn your [Make](https://www.make.com/) scenarios into callable tools for AI assistants.
Capabilities6 decomposed
make scenario exposure as mcp tools
Medium confidenceExposes Make.com automation scenarios as callable tools through the Model Context Protocol (MCP), enabling AI assistants to invoke pre-built workflows without direct API knowledge. The MCP server acts as a bridge that translates scenario definitions into tool schemas that LLMs can discover and invoke, handling authentication to Make's API and parameter mapping between the assistant's tool calls and Make's scenario execution format.
Directly bridges Make.com's proprietary scenario model to MCP's tool schema standard, allowing any MCP-compatible LLM to discover and execute Make workflows without custom adapter code. Uses Make's native API to dynamically generate tool definitions from live scenario metadata rather than requiring manual tool registration.
Simpler than building custom Make API clients for each AI framework because it leverages MCP's standardized tool interface, making Make scenarios portable across Claude, custom agents, and other MCP-compatible systems.
dynamic scenario discovery and schema generation
Medium confidenceAutomatically discovers all accessible Make scenarios from a user's Make account and generates MCP-compatible tool schemas by querying Make's API for scenario metadata, input/output definitions, and execution parameters. The server introspects scenario structure and converts it into JSON Schema format that LLMs can understand, eliminating manual tool registration and keeping schemas in sync with scenario changes.
Performs real-time introspection of Make scenarios via API to generate tool schemas, rather than requiring manual schema definition or static configuration files. Automatically maps Make's parameter types and constraints to JSON Schema, enabling LLMs to understand scenario inputs without developer intervention.
More maintainable than static tool registries because scenario changes in Make automatically propagate to the MCP server without code updates, reducing schema drift and configuration debt.
scenario execution with parameter mapping
Medium confidenceTranslates LLM tool calls into Make scenario executions by mapping the assistant's structured parameters to Make's execution API format, handling type coercion, validation, and payload construction. The server receives tool invocations from the MCP client, validates parameters against the scenario's schema, constructs the appropriate Make API request, and returns execution results back to the LLM in a standardized format.
Implements a parameter mapping layer that translates between MCP's generic tool call format and Make's scenario-specific execution API, including type validation and payload construction. Handles the impedance mismatch between LLM-friendly parameter schemas and Make's internal execution format.
More robust than direct Make API calls from LLMs because it validates parameters against the scenario schema before execution, reducing failed API calls and improving reliability in agentic workflows.
mcp protocol compliance and tool discovery
Medium confidenceImplements the Model Context Protocol (MCP) server specification, exposing Make scenarios as tools that MCP-compatible clients (Claude, custom agents, etc.) can discover and invoke. The server handles MCP's tool listing, tool calling, and result streaming protocols, translating between MCP's standardized tool interface and Make's scenario execution model.
Fully implements MCP server specification, allowing Make scenarios to be exposed as first-class tools in any MCP-compatible system. Handles MCP's protocol details (tool listing, invocation, error handling) transparently, abstracting away Make API complexity from the MCP client.
More portable than custom Make API wrappers because MCP is a standardized protocol; the same server works with Claude, custom agents, and future MCP-compatible systems without modification.
authentication and credential management
Medium confidenceManages Make API authentication by storing and using API credentials (tokens or OAuth) to authorize requests to Make's API on behalf of the user. The server handles credential initialization, token refresh if needed, and secure transmission of authentication headers to Make, ensuring that tool execution requests are properly authenticated without exposing credentials to the LLM or MCP client.
Centralizes Make API authentication at the MCP server level, allowing the server to handle credential management and token lifecycle without exposing credentials to the LLM or MCP client. Separates authentication concerns from tool execution logic.
More secure than embedding Make credentials in LLM prompts or client code because credentials are managed server-side and never transmitted to the AI assistant, reducing exposure surface.
error handling and execution status reporting
Medium confidenceCaptures and reports errors from Make scenario executions, including API failures, parameter validation errors, and scenario execution failures, translating them into human-readable messages that the LLM can understand and act upon. The server distinguishes between different error types (client errors, server errors, timeout) and returns structured error responses that include context for debugging and recovery.
Translates Make API errors into structured, LLM-friendly error responses that include error type, message, and context, enabling AI assistants to reason about failures and decide on recovery strategies. Distinguishes between different error categories (validation, execution, API) to provide actionable feedback.
Better for agentic workflows than raw Make API errors because it provides structured error information that LLMs can parse and reason about, enabling intelligent error recovery and retry logic.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
Artifacts that share capabilities with Make, ranked by overlap. Discovered automatically through the match graph.
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testing
MCP server: testing
1mcpserver
** - MCP of MCPs. Automatic discovery and configure MCP servers on your local machine. Fully REMOTE! Just use [https://mcp.1mcpserver.com/mcp/](https://mcp.1mcpserver.com/mcp/)
@opvs-ai/mcp
OPVS MCP Server — all 6 public OPVS skills (AgentBoard, AgentDocs, AgentMemory, OPVS Protocol, Auth, Integrations) in one MCP. For clients without per-MCP tool caps (Claude Code, Cursor). Antigravity users should use the scoped @opvs-ai/mcp-<skill> packag
Best For
- ✓Teams using Make.com for workflow automation who want to integrate with AI assistants
- ✓Developers building AI agents that need to orchestrate existing Make scenarios
- ✓Non-technical users who have built Make workflows and want AI-driven execution
- ✓Teams with many Make scenarios who want dynamic tool availability
- ✓Developers who frequently modify Make workflows and need schema updates reflected automatically
- ✓Organizations using MCP servers as a central integration point for multiple AI assistants
- ✓AI agents that need to execute Make workflows as part of multi-step reasoning chains
- ✓Teams building autonomous systems that orchestrate Make automations
Known Limitations
- ⚠Requires pre-existing Make scenarios — cannot create new scenarios from the MCP server
- ⚠Tool discovery depends on Make API returning accurate scenario metadata; schema generation is limited to what Make exposes
- ⚠No built-in retry logic or error recovery — failures in Make execution propagate directly to the LLM
- ⚠Authentication is static per server instance — no per-user credential isolation
- ⚠Schema generation accuracy depends on Make API's metadata completeness — complex nested parameters may not translate perfectly to JSON Schema
- ⚠Discovery latency: initial scenario enumeration and schema generation adds startup time proportional to scenario count
Requirements
Input / Output
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** - Turn your [Make](https://www.make.com/) scenarios into callable tools for AI assistants.
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