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The Integrate.io MCP server exposes a set of package-authoring tools that AI agents can call to build a working ETL package end to end. Supported clients include Claude Desktop, Claude Code, Cursor, and the MCP Inspector. The agent discovers what the catalog supports, composes a dataflow JSON, creates the package, fixes wiring errors in place, and runs it. No web UI required. These tools complement the read and run tools described in the MCP Server overview. Configure your client and mint a token there first.

When to use these tools

  • You want an AI agent to build a new source-to-destination package in a single tool call from a natural-language prompt.
  • You want the agent to clone an existing pipeline as a template, then swap in different connections, tables, or paths.
  • You want the agent to add, edit, or remove components on an existing pipeline without opening the package designer.
  • You want the agent to rename a package or define its public variables in place.
  • You want the agent to fix a broken edge or component without opening the package designer.
  • You want the agent to detect the columns of a CSV or other delimited file on an SFTP, S3, or GCS connection before wiring it into a flow.
  • You want the agent to archive packages that failed an authoring attempt so they don’t clutter the active list.
If you only need read or run access, stick to the inspection and execution tools on the overview page.

Tool reference

build_pipeline (mutation)

Builds a complete source → destination pipeline in one call from a high-level intent. The server discovers the source schema, picks the matching component types for each connection, wires source → select → destination with edges, maps fields 1:1, and persists the package. The agent only passes intent (which connections, which table or file path, which fields, any computed columns), so the build turn stays fast even on wide schemas. Prefer build_pipeline over create_package for a straightforward “load X into Y” build. Use create_package or add_package_components when the shape isn’t covered here: filters, joins, multiple sources, a file or cloud-storage destination, or edits to an existing package.
name
string
required
Package name shown in the dashboard.
source
object
required
Source intent. See below.
destination
object
required
Destination intent. Must be a database or warehouse connection (for example Snowflake, Redshift, BigQuery, MySQL, Postgres). File and cloud-storage destinations are not yet supported here, use create_package for those.
fields
"all" or array
default:"\"all\""
"all" (default) carries every discovered source column. An array of source column names keeps only that subset.
computed
array
Derived columns to append. Each entry: { "name": "<output column>", "expression": "<Pig expression>" }. A computed name equal to a source column overrides that column.
flow_type
string
default:"dataflow"
dataflow (default) or workflow.
workspace_id
integer
Must belong to the calling account. Omit to create the package as Unassigned.
description
string
Free-form, up to 4096 characters.
source accepts:
  • connection_id (integer, required).
  • path (string) for file or cloud-storage sources (SFTP, S3, GCS, SharePoint).
  • table_name (string) for database or warehouse sources.
  • schema_name (string), optional, database sources only.
  • delimiter (string), file field delimiter, default ,.
  • header_row (boolean), whether the file has a header row, default true.
  • source_path_field_alias (string), optional, file and cloud-storage sources only. When set, adds an output column with this exact name carrying the source file path each row was read from. Use this when the agent wants the filepath as a column for lineage. Never hardcode the literal path as a field value.
destination accepts:
  • connection_id (integer, required).
  • table_name (string, required).
  • schema_name (string), optional.
  • operation_type (string), write mode (for example append, truncate_and_insert, merge). Defaults to append. Validated against the destination’s allowed write_modes from describe_component_type.
  • create_table (boolean), auto-create the table, default true.
  • merge_keys (array of string), required for merge and upsert modes (merge, merge_update_and_insert, insert_or_update). Each name flags the matching destination column with is_merge_key: true. The platform never infers the key, so a merge without merge_keys will not upsert correctly. Append, overwrite, and truncate modes ignore this field.
Field names from the source are carried through exactly as discovered, no renames, recasing, or added prefixes. The tool returns the new package_id and also runs validate_package automatically so the caller knows whether the build is clean on the first response.

Example: SFTP to Snowflake with a merge key and filepath column

Error envelopes

create_package (mutation)

Mints a new package from a full data_flow_json spec. Components and edges are persisted in a single transaction, partial packages are never written.
name
string
required
Package name shown in the dashboard.
flow_type
string
required
dataflow or workflow.
components
array
required
Array of single-key wrapper hashes (see below).
edges
array
required
Array of edge hashes referencing component ids.
workspace_id
integer
Must belong to the calling account. Omit to create the package as Unassigned.
description
string
Free-form, up to 4096 characters.
flow_version
string
default:"2.0.0"
Defaults to 2.0.0 (the modern package designer).
variables is not accepted. Packages that need package-level variables must be created through the REST API.

Component shape

Each component is a single-key wrapper hash. The key is the wrapper type (for example database_source_component), and the inner hash carries the configuration. Common fields:
  • id, stable identifier referenced by edges. Convention is component-<hex>. Omit to let the server generate one.
  • name, human-readable label shown on the canvas.
  • xy, [x, y] position pair.
  • alias, data-flow alias. Sources expose this; destinations consume it via input_alias.
  • connection, connection metadata block. Uses the generic key connection, not type-specific keys. Shape: { "id": <integer>, "name": "<display name>", "type": "<connection-type-slug>" }. Without name and type, the dashboard can’t render the connection chip. When update_package_components changes a *_connection_id, the server clears this block and re-embeds it from the new connection so the dashboard chip stays in sync with the connection the job will run.
  • <type>_connection_id, string form of the connection id (for example "cloud_storage_connection_id": "47376").
  • specificComponentType, vendor sub-type (for example sftp_source_component, mysql_source_component). Required when the wrapper covers multiple vendors so the dashboard renders the correct icon and form.
  • schema, nested object { "fields": [{ name, alias, data_type }, ...] }. Each field needs an alias.

Edge shape

Edges reference components by their id field:
id and label are optional, the server generates them when absent.

Best practice: clone from an existing package

The safest way to compose a valid data_flow_json is to read a similar existing package first:
  1. Call list_packages and find a pipeline with the same source and destination connection types.
  2. Call get_package(<that_id>, include_full_graph: true) and inspect its data_flow_json.
  3. Use the result as a template. Swap in your own connection ids and table or file paths. Keep the structural keys.
Inline secrets in the source package (REST api_key query params, Authorization headers, basic-auth passwords, and similar) come back as [REDACTED] in the full graph. Structure and non-secret fields are preserved, so the result is still a valid template, refer real credentials through a connection or a secret variable when you re-author the package. See Inline secret redaction in get_package. Building from describe_component_type output alone is error-prone, it surfaces attr_accessor property names but can’t expose nested sub-shapes (like schema.fields) or renderer conventions like specificComponentType. Pair it with a working template whenever possible.

Returns

On success: { package_id, package_version, name, flow_type, flow_version, workspace_id, created_at, warnings }. The warnings array lists shape fixups the server applied (for example rewriting a vendor-named wrapper to the canonical wrapper plus specificComponentType). On failure: { error: "..." }, workspace not in account, name too long, save failure, and so on.

clone_package (mutation)

Mutation. Copies an existing package into a NEW package to use as a template, without editing the original. The clone copies the source’s data_flow_json, variables, description, and flow type, is attributed to the calling user, and gets a new package_id. For workflow packages, sub-package references inside data_flow_json are preserved as-is, not recursively deep-cloned.
Use clone_package to START a new package from an existing one, not to edit the original. If the user wants to add, edit, or rewire components on a package that already exists, use add_package_components, update_package_components, or update_package_edges. Cloning (or clone-then-archive) to make an edit mints a new package_id and orphans the original’s schedules, run history, and API references.
source_package_id
integer
required
Package to copy.
target_name
string
required
Name for the new package (up to 128 characters).
target_workspace_id
integer
Workspace to place the clone in. Must belong to the calling account. Defaults to the source package’s workspace.
target_description
string
Description for the new package, up to 1024 characters. Defaults to the source package’s description.
dry_run
boolean
default:"false"
Return what would be created without writing.
Returns { new_package_id, source_package_id, name, flow_type, workspace_id, owner_id, created_at } on success.

add_package_components (mutation)

Appends one or more NEW components (and, optionally, new edges) to an existing package’s flow. This is the “extend a package the user already has open” operation, for example “add a Select component” or “insert a Filter between the source and destination.” It fills the gap between create_package (builds a whole package) and update_package_components (only edits components that already exist).
Do not recreate a package, or clone-then-archive it, to add to it. That mints a new package_id and orphans the original’s schedules, run history, API references, and the user’s open editor session. add_package_components edits the package in place.
package_id
integer
required
Package to extend.
components
array
required
One or more single-key wrapper hashes, the same shape create_package uses. See Component shape.
edges
array
New edges wiring the added components in. Omit to add components unwired, then call update_package_edges.
Each component follows the same wrapper shape as create_package. Two placement details are specific to adding:
  • name must be unique within the package, it is how update_package_components later targets the component.
  • xy is optional when the component is wired to an existing one: the server places a newly-added component just below its parent and nudges the downstream chain down to make room. Provide an explicit xy only for a standalone component with no incoming edge (for example a new source).
Carry field names through exactly as discover_schema, discover_file_schema, or preview_data returned them, same spelling and case. Never add an f_ prefix or rename a field; the platform makes Pig aliases valid and reserved-word-safe automatically. The call is all-or-nothing and row-locked: if any new component’s name or id collides with one already in the flow, nothing is written. Each successful call bumps the package version via PaperTrail and is reversible from the version history UI. Returns { package_id, components_added, edges_added, new_version, updated_at } on success, plus a warnings array listing any wrapper-shape fixups the server applied.

update_package_components (mutation)

Mutation. Edits specific components that ALREADY EXIST in a package’s data_flow_json. Each entry is { component_name, updates }, where component_name matches the inner name field on a component and updates is a hash of fields to merge onto it. Untouched top-level fields are preserved, and nested hashes are merged recursively (deep merge), so a partial config update does not drop sibling keys. To ADD a new component use add_package_components, to REMOVE one use remove_package_components, and to re-wire edges use update_package_edges.
package_id
integer
required
Package to update.
component_updates
array
required
At least one entry. Each entry is { component_name, updates }: component_name matches a component’s inner name, and updates is a hash of fields to deep-merge onto that component. The name and id fields are protected and cannot be overwritten, since edges reference them. Do not place credentials in updates; connection secrets live in connection management, and putting them in data_flow_json leaks them through the version history.
dry_run
boolean
default:"false"
Report which components would change without writing.
The call is all-or-nothing: if any component_name is not found in the flow, the entire update is rejected and nothing is persisted. When an updates hash changes a *_connection_id, the server clears the stale connection display block and re-embeds it from the new connection so the dashboard chip stays in sync. The package row is locked (SELECT FOR UPDATE) for the save so concurrent agent calls serialize correctly. Each successful call bumps the package version via PaperTrail and is reversible from the version history UI. Returns { package_id, components_updated, new_version, updated_at } on success.

list_component_types (read)

Enumerates every component registered in the platform catalog. Returns one entry per concrete subclass with:
  • name, internal name (for example mysql_source_component). Pass this to describe_component_type.
  • category, source, transformation, or destination.
  • class_name, Ruby class name, informational.
  • description, header docstring from the component’s source file.
  • wrapper_key, the outer key to use in data_flow_json. May be null for transformations or unmapped classes.
  • specific_component_type, value for the inner specificComponentType field. May be null when the vendor has its own top-level wrapper.
Optional category argument filters the result. Use this when the account has no similar package to clone from. For mature accounts, get_package of an existing pipeline is often enough.

describe_component_type (read)

Per-component introspection by internal name. Returns:
  • properties, writable attribute names extracted from attr_accessor declarations.
  • required, attribute names with presence validators.
  • description, header docstring.
  • example, fixture JSON example, or null if none exists.
  • wrapper_key, specific_component_type, valid_specific_types, wrapper_example, copy-paste-ready wrapper recipe.
Required argument: type (the name from list_component_types). Returns { error: "unknown component type: <type>" } for unknown names.

update_package_edges (mutation)

Replaces a package’s edges array wholesale. Pass the complete desired edges array, existing edges are overwritten.
package_id
integer
required
Package to update.
edges
array
required
Full edges array. Each edge needs at minimum source and target matching a component id in the current package.
The tool pre-validates every source and target against the package’s current components. If any edge references an unknown component, the entire call is rejected, no partial writes. Each write bumps the package version via PaperTrail and is reversible through the existing UI history. Use this when validate_package surfaces an edge-related error after create_package and you need to fix wiring in place. For component-internal edits (changing a connection, adjusting a schema), use update_package_components.

remove_package_components (mutation)

Removes one or more components from an existing package’s flow by component name. Any edge whose source or target was a removed component is dropped at the same time, so the package never ends up with dangling edges.
package_id
integer
required
Package to update.
component_names
array
required
Inner name values from the package’s components. The same key update_package_components targets.
The call is all-or-nothing. If any name doesn’t match a component currently in the flow, the entire call is rejected with the list of unknown names and nothing is removed. The package row is locked for the read-modify-write, and each successful call bumps the package version via PaperTrail and is reversible from the version history UI. Use this to undo a mis-added component or to clean up an unused branch. To add a component, use add_package_components. To edit one in place, use update_package_components. To archive the entire package, use delete_package. Returns { package_id, components_removed, edges_removed, new_version, updated_at } on success.

rename_package (mutation)

Renames a package and, optionally, updates its description. Metadata-only. data_flow_json (components and edges) is left untouched.
package_id
integer
required
Package to rename.
name
string
required
New package name (3 to 128 characters).
description
string
Free-form, up to 1024 characters.
The change is attributed to the calling user and recorded in the package version history, the same as a rename through the dashboard. Use this when an agent needs to retitle a package after a prompt-driven authoring step rather than asking the user to open the UI. Returns { package_id, previous_name, name, updated_at } on success.

manage_package_variables (mutation)

Defines, updates, or removes a package’s public variables. Public variables are the named defaults a package exposes; per-run overrides are still passed through run_package’s variables argument.
package_id
integer
required
Package to update.
set
object
Hash of { variable_name => default_value } to add or update. Values are stored as given; for Pig string literals, follow the embedded single-quote rule used by run_package. At least one of set or remove is required.
remove
array
Variable names to delete. At least one of set or remove is required.
This is a partial update of the variables store, modeled on the dashboard’s Variables modal. The pipeline graph is not touched. The package row is locked for the read-merge-write so a concurrent dashboard save can’t drop a just-set variable. Secret variables are intentionally not supported here. They are encrypted at rest and must be set through the dashboard so plaintext never flows through the MCP transcript. Putting a secret value in set would store it as a plaintext public variable. Returns { package_id, variables } (the full resulting public-variable map) on success. File-schema detection is covered by discover_file_schema on the Reading & Inspecting page.

delete_package (mutation)

Archives a package, same semantics as the dashboard’s Archive action. The Job row is preserved with status archived, removed from the active package list, and remains queryable via list_packages(status: 'archived').
package_id
integer
required
Package to archive.
If any active schedule still references the package, the archive transition is rejected. Disable the schedule first with toggle_schedule(enabled: false). Use this to clean up after an unsalvageable create_package attempt so failed iterations don’t accumulate as dead rows. Returns { package_id, status: 'archived', archived_at } on success, or { error: "..." } for package-not-found or rejected transitions.

Version history

Every write through the package-authoring tools is versioned via PaperTrail. These two tools let an agent inspect that history and roll back a change.

list_package_versions (read)

Read-only. Lists a package’s saved version history (PaperTrail snapshots), oldest first, with the current state last. Summary only, not the full data_flow_json of each version. Up to 100 entries.
package_id
integer
required
The package (job) id.
versions
array
Each entry: version (the index to pass to revert_package), package_version, description, author, created_at, component_count, and current. To undo the most recent change, use the highest-numbered current: false entry (the one immediately before the current entry).

revert_package (mutation)

Mutation. Forward-only rollback to a prior version returned by list_package_versions. It restores the chosen version’s data_flow_json as a NEW version, so the revert is itself undoable, and leaves the package’s schedules and run history untouched.
package_id
integer
required
Package to revert.
version
integer
required
The index of a list_package_versions entry whose current is false. That list is oldest-first with the current state last, so to undo the most recent change pick the highest-numbered current: false entry (the one immediately before the current entry). Never pass the current entry.
dry_run
boolean
default:"false"
Report what would be reverted without writing.
Returns { package_id, reverted_to_version, restored_from_package_version, new_package_version, updated_at } on success.

Example: create a minimal SFTP-to-S3 package

A successful response returns the new package_id. Pass it to validate_package next, fix any errors with update_package_components or update_package_edges, then run with run_package.

Error envelopes

Mutating tools return a plain { "error": "..." } object instead of a JSON-RPC error when the failure is expected and recoverable. Common cases: Unrecoverable failures (auth, malformed JSON-RPC) return standard JSON-RPC error responses, see the MCP Server overview for HTTP-level errors.

Audit trail

Every write performed through these tools is captured in your account’s audit history via PaperTrail. Package creates, edge updates, and archive transitions all record the calling user as the author. Component edits made by update_package_components are versioned and reversible through the existing package version history UI.
Last modified on July 14, 2026