Encapsulate Celigo integration logic behind callable input/output contracts for MCP tools.
npx skills add https://github.com/celigo/ai --skill building-tools
Please help me install this Agent Skill. Skill: building-tools Source: https://github.com/celigo/ai/tree/main/skills/building-tools Command: npx skills add https://github.com/celigo/ai --skill building-tools
Building the same lookup–transform–import–branching logic repeatedly across flows, APIs, agents, and MCP servers wastes time and creates inconsistent behavior.
Ask the AI to help you define a tool with an input JSON Schema, wire required export/import page processors into router branches, and configure response mapping and output mappings so the tool returns a validated result.
High-intent search queries and answers about installing and using this skill.
You can expose Celigo integrations as callable tools by encapsulating lookup, import, and transform logic behind input/output contracts and configuring them for MCP server exposure. This allows AI agents to call the tools as external endpoints.
Reusing integration logic across flows and APIs involves encapsulating lookup, transform, and branching processes behind a defined input and output schema. This contract-driven approach ensures consistent behavior without rebuilding logic for each consumer.
Routing and branching are configured using a router with first-matching selection logic to choose branches. Sequential page processors then execute export and import operations, applying response mapping before returning the final output.
Yes, defining a JSON schema is required to build a tool. Tool input schema validation requires a root type object for MCP compatibility, ensuring the callable input and output contracts are strictly enforced.
You should build a reusable tool when the same lookup or import logic is needed by two or more consumers, when connection flexibility is required, or when you must expose integration logic via an MCP server.
Sequential page processors execute export and import operations, after which response mapping is applied to process the results. Final output mapping then transforms the data to match the declared output schema required by the caller.
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