Build Celigo tool resources -- reusable building blocks that encapsulate lookups, imports, transforms, and branching behind input/output contracts. Callable from flows, APIs, AI agents, MCP servers, and other tools. Use when creating tools, adding steps, or configuring routing.

<! TIER:1 Building Tools A tool is Celigo's first class reusable building block . It encapsulates logic lookups, imports, transforms, branching behind a defined input and output contract. Build it once, use it everywhere: from Flows, APIs, AI Agents, MCP Servers, and other Tools. Tool Concepts Why tools exist: Without tools, users build the same lookup transform import patterns repeatedly across Flows, APIs, and Agents. Tools solve this by providing a governed, composable abstraction: one definition, many consumers, consistent behavior. When to build a tool: You're building an MCP server MCP servers expose tools as endpoints; every piece of logic an MCP server offers must be a tool The same logic is needed by 2+ consumers (flows, APIs, agents, MCP servers) build once, call everywhere You want connection flexibility callers can pass different connections to the same tool definition You're composing smaller pieces tools can call other tools for nested orchestration When NOT to build a tool: The logic is specific to one flow or API and won't be reused inline it as a lookup/import processor directly You need abstract/instance templating for multi tenant patterns use abstract flows Tool vs API: the split is how the work gets invoked. An API is reachable from outside Celigo over HTTP (a partner system, a customer facing app). A tool is reachable only from inside Celigo (flow steps, AI agents, APIs, other tools). If a recipe must be reachable from both, build it as a tool and expose the tool behind an API endpoint the tool stays available to inside Celigo consumers at the same time. Tool vs flow: tools don't start themselves no schedule , no listeners, no flow runtime controls ( proceedOnFailure and friends are the consumer's concern), and no abstract/instance layer (a tool is already the unit of reuse; per environment variation is handled by the consumer bound connection model). "Every night, do X" or "when a webhook arrives, do Y" is a flow the tool may be the thing the flow does . Architecture: Routers hold branches, branches hold page processors. Use multiple branches when different inputs need different processing paths. Chain routers via nextRouterId for sequential processing stages. The special nextRouterId: "outputRouter" exits the tool and returns results. Tool Execution Pipeline When a tool is invoked: 1. Input received input data validated against the tool's JSON Schema ( input ) 2. Router evaluation routeRecordsUsing evaluates branch conditions (if multiple branches exist) 3. Branch selection matching branch processes the input 4. Page processors each processor executes sequentially (export lookups, import writes) 5. Response mapping responseMapping on each processor carries data to the next processor 6. Output mapping final output mapped according to the tool's output configuration (schema, mappings, lookups, hooks) 7. Return output returned to the caller (flow, API, AI agent, MCP server) Execution mode depends on the caller: Called from a Flow runs in flow mode (batch oriented, run console, error management) Called from an API runs in API mode (single request/response) Called from an AI Agent runs with agent context; agent maps connections to the tool Called from an MCP Server exposed as an MCP tool endpoint for external AI clients Called from another Tool nested execution within the parent tool's context Connection model: Always bring your own keys. The caller (flow, API, agent, MCP server) maps connections to the tool at configuration time. MCP Server overrides can swap connections per server without modifying the tool. Build order: Connection Export + Import Tool (consumer: Flow / API / Agent / MCP Server) Concerns beyond the build steps: Response mapping extract fields from processor responses back into the record. Configured on pageProcessors[] entries within branches, but planned when building the processors. For lookups the response has data[] and errors[] (use data[0].fieldName for single results); for imports use json.fieldName . Uses Transformation 1.0 syntax (extract/generate pairs) postResponseMap hook JavaScript processing after response mapping. Also on pageProcessors[] entries, but planned when building the processors The Two Flavors of Lookup "Lookup" refers to two different things in a tool. They live in different places and solve different problems: Branch page processor lookups ( pageProcessors[].type: "export" ) work doing lookups inside a router branch that call an external system at runtime ("look up the customer in NetSuite by email"). The data isn't in the tool yet; the export fetches it, and a responseMapping pulls fields from the response onto the record so downstream branches and the output can see them. This is the same lookup primitive flows use same export resource, same response mapping, same postResponseMap hook. Output static lookup tables ( output.lookups[] ) declarative value translation tables on the output stage. They reach no external system; they are fixed key/value maps defined inline (e.g. a map translating A to Active , I to Inactive , P to Pending , with a default ). An output mappings[] entry references a table by its name (via lookupName ) to translate a field value during output assembly. Rule of thumb: external system, runtime call branch page processor lookup. Translate one value into another via a fixed table output static lookup. Consumer Bound Connections A tool never pins connections at design time the key difference from flows and APIs: A flow or API pins each of its own steps to a specific connection at build time the connection IDs are baked into the resource. A tool's lookups and imports declare the connections they need , and the consumer supplies the actual connection at bind time when the tool is added as a flow step, attached to an AI agent, exposed behind an API endpoint, or embedded in another tool. The tool definition stays unchanged across every binding. The same tool can be bound with a sandbox NetSuite connection from one consumer and a production NetSuite connection from another, without forking. Mechanically, the tool's page processors reference underlying export and import resources that carry the connection; binding selects which connection records those resources use in that context. Because of this, knowing which connections a tool requires is part of its design those connections must already exist in the consumer's account before the tool can be bound and run there. Quick Reference Decision Matrix You need to... Build a tool? Instead use Reuse logic across 2+ flows/APIs/agents Yes Expose logic via MCP server Yes Allow callers to swap connections Yes Nest orchestration (tool calls tool) Yes One off logic for a single flow No Inline lookup/import in flow Multi tenant templating No Abstract/instance flows Minimum Required Fields Every tool needs at minimum: name , integrationId , and an input.schema . Which Schemas to Read 1. Always: [request.yml](references/schemas/request.yml) (base fields for create/update) 2. Input config: [input.yml](references/schemas/input.yml) (schema, transform, mockInput) 3. Output config: [output.yml](references/schemas/output.yml) (mappings, lookups, hooks) 4. Pipeline config: [router.yml](references/schemas/router.yml) (routing strategy, branches, page processors, response mapping) 5. Response shape: [response.yml](references/schemas/response.yml) Schema Index All schemas are in [references/schemas/](references/schemas/): Base fields (create/update): [request.yml](references/schemas/request.yml) Response shape: [response.yml](references/schemas/response.yml) Input configuration: [input.yml](references/schemas/input.yml) schema, transform, mockInput Output configuration: [output.yml](references/schemas/output.yml) mappings, lookups, hooks Router and branch configuration: [router.yml](references/schemas/router.yml) routing strategy, branches, page processors, response mapping Related Skills [configuring exports Quick Reference](../configuring exports/SKILL.md quick reference) building lookup exports used as steps in the tool pipeline [configuring imports Quick Reference](../configuring imports/SKILL.md quick reference) building imports used as action steps in the tool pipeline [building flows How to Build a Flow](../building flows/SKILL.md how to build a flow) wiring tools into flow pipelines [building apis Quick Reference](../building apis/SKILL.md quick reference) exposing tools via API endpoints [writing scripts Quick Reference](../writing scripts/SKILL.md quick reference) script hooks on tool page processors [writing handlebars Quick Reference](../writing handlebars/SKILL.md quick reference) dynamic expressions in request bodies, URIs, and field mappings [configuring filters Quick Reference](../configuring filters/SKILL.md quick reference) input filters on tool steps and router branches <! TIER:2 How to Build a Tool 1. Determine the tool's purpose What should this tool do when called? Define the inputs it expects and the processing steps it needs. 2. Identify the integration Every tool belongs to an integration. Find or create the integration first. 3. Check for existing patterns Before building from scratch, look at what already exists: The account index auto refreshes when stale ( 4 hours). Force a fresh snapshot with celigo account snapshot . Existing tools in the account are the best reference they show proven patterns for that specific customer's setup. Marketplace templates may provide a complete pre built integration you can install rather than building from scratch. 4. Build the connections, exports, and imports Tools reference exports and imports as page processors in router branches. Build bottom up: connections first, then exports and imports that use those connections, then the tool that wires them together. See configuring exports and configuring imports . 5. Define the input schema The input schema is a JSON Schema object describing what data the tool accepts. For MCP compatibility, the root schema must have type: "object" . 6. Configure routing (if needed) No routing all inputs processed the same way; skip routers entirely Filter based routing ( routeRecordsUsing: "input filters" ) declarative expression rules on each branch Script based routing ( routeRecordsUsing: "script" ) custom JavaScript function returns the branch name 7. Wire page processors into branches Each branch contains pageProcessors[] an ordered list of exports (lookups) and imports (actions). Each processor has: type : "export" or "import" exportId or importId : reference to the resource responseMapping : extract fields from the processor response back into the record hooks.postResponseMap : optional script for post processing proceedOnFailure : whether to continue if this step fails 8. Configure output Output mappings transform the processed data into the tool's return value. Supports: mappings[] extract/generate field pairs (Celigo standard mapping format) lookups[] static key value enrichment tables hooks.preMap / hooks.postMap script hooks before and after mapping Output mappings and branch response mappings both shape data, but at different times. Branch responseMapping merges a processor's response onto the in flight record so downstream branches and routers can use it. Output mappings assemble the tool's return value at the very end and they only see the final in flight record, not raw processor responses. If a processor

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