create_workspaceCreate a new workspace, owned by the authenticated user. Use this if list_my_workspaces returns none.
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | Workspace name | |
| description | No | Optional workspace description. |
| Name | Required | Description | Default |
|---|---|---|---|
| name | Yes | ||
| createdAt | Yes | ||
| createdBy | Yes | ||
| permission | Yes | ||
| description | No | ||
| permissions | Yes | ||
| workspaceId | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations (readOnlyHint: false, idempotentHint: false) communicate the non-idempotent write semantics. The description adds the ownership context, which is a useful behavioral trait. However, it stops short of disclosing edge cases like duplicate name errors or quota limits. The addition is decent but not deep.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two succinct sentences with zero fluff. The first states the action and ownership; the second conveys a critical usage condition. Both sentences are information-dense and earn their place in the description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 2-parameter tool with an output schema and simple semantics, the description covers the essentials: action, ownership, and the primary use case condition. Exposing failure modes (e.g., duplicate names) would push it higher, but the current scope is well-handled and unimpeded by missing context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% (both 'name' and 'description' fields are documented in the schema). The description adds no parameter-level semantics, but given the full schema coverage, the baseline of 3 is the correct score per the rubric.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb+resource pairing ('Create a new workspace') and clarifies ownership ('owned by the authenticated user'). It's clear what the tool does, though it doesn't explicitly differentiate from siblings (not that any overlap is apparent).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The instruction 'Use this if list_my_workspaces returns none' provides an explicit when-to-use trigger and directs the agent to a related sibling tool. This is exactly the kind of concrete conditional guidance agents need and is a best-in-class example of usage context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools map to distinct lifecycle phases and the descriptions explicitly separate overlapping-sounding concepts, such as list_data versus submit_query and the generic call_dpf_api from dedicated tools. The three finish_* tools are similarly worded but each is clearly tied to a specific preceding operation, so confusion should be limited.
The tool names are uniformly snake_case and mostly follow a readable verb_noun pattern like delete_data_spec, create_workspace, and run_data_job. It is not a perfect 5 because broader names like manage_connection and manage_trigger, the generic call_dpf_api, and list_my_workspaces with its pronoun make the naming pattern less predictable.
At 16 tools, the set is just slightly above the ideal range, but the tools generally earn their place by representing distinct steps or workflow boundaries. The start/finish pairs create some apparent redundancy, but that is a natural consequence of the multi-step file-upload flow.
The toolset provides solid coverage of the core data-platform lifecycle: workspaces, data specs, jobs, connections, triggers, scheduled pulls, status polling, and SQL querying. Some additional DPF capabilities are only reachable through the generic call_dpf_api rather than dedicated tools, and billing mutations are explicitly left outside the MCP surface, so coverage is strong but not absolute.