contactSend a message to the DPF team — request a demo, ask about licensing, report an issue, or request a feature. No authentication required. Always ask the user for their email if they have not already given it in this conversation.
| Name | Required | Description | Default |
|---|---|---|---|
| name | No | ||
| Yes | The sender's email address, so DPF can reply. | ||
| reason | Yes | ||
| message | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations are all false, providing no hints about read-only, destructiveness, or idempotency. The description does not disclose side effects, confirmation, or what happens after the message is sent. It only mentions lack of authentication, but this is more of a prerequisite than a behavior. Transparency is limited.
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?
The description is concise and well-structured, with one main sentence followed by two short clauses. No unnecessary information is included, and it is easy to read.
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?
No output schema is present, so return values are not expected. However, the description does not explain what happens after sending (e.g., confirmation, response time) or the meaning of the reason enum values. Given the low parameter coverage and lack of behavioral details, the description is incomplete for full 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 coverage is only 25% (only email has a description). The other parameters (name, reason, message) lack any semantic explanation beyond their types. The enum for reason is self-explanatory, but its allowed values are not elaborated. Overall, parameter meanings are poorly specified.
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 clearly states the tool's purpose: sending a message to the DPF team, and enumerates specific use cases (demo request, licensing, issue, feature). This is specific and distinguishable from sibling tools.
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 description provides practical usage instructions: no authentication required and a directive to always ask for the user's email if not already provided. It does not explicitly mention when not to use the tool, but the guidance is sufficient for most scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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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.