Tool catalogue
Find the tools available in your DataMaker session.
The connected server announces the authoritative tool names, arguments and response schemas. Catalogue coverage depends on the service, deployment and caller; this page groups common capabilities rather than claiming a fixed tool count.
Core metadata and generation helpers
| Tools | Purpose |
|---|---|
get_templates, get_template_by_id | Discover and inspect templates. |
create_template, create_template_from_json, create_template_from_csv | Propose or save template definitions. |
generate_from_id | Generate using a saved template. |
get_connections, get_connection_by_id | Discover database connections. |
get_endpoints, get_endpoint_by_id, list_integrations | Discover saved requests and systems. |
get_scenarios, get_scenario_by_id, save_scenario | Inspect or save reusable Python workflows. |
get_scenario_environment_variables | Inspect scenario configuration in the authorized context. |
get_sets, get_set_by_id | Inspect reusable sets. |
list_validation_flows, get_validation_runs, get_validation_status | Inspect reviewed validation flows, results and evidence. |
start_validation_run | Queue a read-only validation of one eligible saved case. |
list_datasets, get_dataset, dataset_preview, dataset_sample, dataset_stats | Inspect chunked data and bounded summaries. |
list_plans, get_plan | Inspect reviewed specifications. |
list_masking_policies, get_masking_policy | Inspect masking policies and their field rules. |
create-skill, list_skills, get_skill | Work with reusable agent instructions. |
ask_user | Request missing information through the chat UI. |
The metadata service includes selected generation helpers; other execution capabilities live on the execution services. Read the specific tool schema rather than inferring parameters from a similar REST route.
Plans and execution
The plan authoring service exposes create_plan and update_plan; update_plan patches an existing plan by block, and any edit to a plan's definition returns it to draft for re-approval. Execution services provide supported scenario execution, plan runs, data jobs, schema discovery, masking and key-map workflows. See Plans, Data jobs and Masking for their lifecycle and approval requirements.
Masking has three execution tools, and the difference between them is what they touch:
| Tool | What it does |
|---|---|
dry_run_masking_policy | Rehearses a policy against sample rows you supply. Nothing is persisted, and the report names uncovered fields. |
start_mask | Runs the policy over a ready dataset as a data job, writing a new masked dataset. |
unmask_values | Translates tokens back to originals. Only tokenize rules mint reversible values, and every call needs a human approval granted for that specific key map. |
Writing a policy stays a human step in Masking: its rules decide what happens to real values, and an unlisted field passes through a mask job unchanged.
System tools
Registered system namespaces provide the corresponding capabilities:
- SAP: metadata, service/entity discovery, filtered reads and single-record export.
- Tosca: test designs, Cloud execution evidence and supported on-premises object operations.
- Atlassian: Jira and Xray Cloud discovery and test-management operations.
- Other registered systems: inspect the connected catalogue for the supported surface.
Read Connections for setup. A missing tool can mean the service is not connected, disabled or unavailable in that release; it does not justify inventing a tool name.
Check a call
Before execution, confirm the active project, target ID, row count and required inputs. Afterward, inspect the result and any pending approval or error. Long-running operations can return a job identifier that needs polling; a tool returning successfully does not necessarily mean the underlying job completed.