mcp-server

List data specs or jobs

list_data

Read-onlyIdempotent

List either the data specs (parsing + mapping rule sets, resource: "specs") or the data processing jobs (executions of a spec, resource: "jobs") defined in a workspace. Each spec includes its specId and current status — poll a specific one with get_status. Both resources are paginated (default 25/page, max 100, newest first); pass the returned nextCursor to fetch more.

This is NOT a table listing — specs describe configured pipelines (parsing/mapping rules), not the live set of Iceberg tables in the workspace. Multiple specs can target the same table (e.g. one spec creates it, another merges more data into it), and specs can be deleted or fail without the underlying table being dropped. For "what tables exist in my workspace" or any question about actual current data, use submit_query with SHOW TABLES instead of inferring an answer from specs.

Input Schema

TableJSON Schema

Name	Required	Description
`cursor`	No	Opaque `nextCursor` from a prior page (omit for the first page).
`pageSize`	No	Records per page (default 25).
`resource`	Yes	Which kind of resource to list
`workspaceId`	No	Workspace to act on. Defaults to your only workspace if you have exactly one.

Output Schema

TableJSON Schema

Name	Required	Description
`jobs`	No	Present when resource is "jobs"
`count`	Yes	Number of items in this page, not the workspace total
`specs`	No	Present when resource is "specs"
`pageSize`	No
`nextCursor`	No	Opaque cursor for the next page; null when exhausted

TDQS

A4.7/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false, so the description need not repeat safety. It adds useful behavioral context: pagination defaults (25/page, max 100, newest first), use of nextCursor, and the important semantic distinction that specs are not tables. Does not contradict annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two paragraphs; the first is a direct action statement with resource options and pagination; the second clarifies a common misapplication. Every sentence adds necessary information, no fluff, and the key usage is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a dual-resource list tool with pagination and a frequent misuse case, the description covers all essentials: what it lists, page size/cursor mechanics, delineation from table lists, and a pointer to get_status. Given the detailed output schema exists, not describing return format is acceptable; the description is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% as all parameters have descriptions, so baseline is 3. The description adds value by explaining the resource enum meaning (specs vs jobs), the pagination behavior with cursor, and the workspaceId default, which enriches interpretation beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool lists either data specs (parsing + mapping rule sets) or data jobs, specifying the resource parameter values. It distinguishes from siblings by explicitly naming get_status for polling and submit_query for table queries, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit when-to-use guidance: for listing specs or jobs, and when-not-to-use: 'NOT a table listing' and directs users to submit_query with SHOW TABLES for actual table existence. Also mentions polling with get_status for specific spec status, covering alternatives and exclusions.

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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TDQS

A4.2/5.0

Disambiguation4/5

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.

Naming Consistency4/5

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.

Tool Count4/5

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.

Completeness4/5

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.

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