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getsimba-ai

Simba MCP Server

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by getsimba-ai

list_uploads

List datasets in your workspace, newest first, with file details like ID, row count, and source. Filter by filename and page with limit/offset to locate the upload you need.

Instructions

List the datasets in your workspace (newest first) — every source, not just API uploads: dashboard/manual uploads and pipeline-ingested datasets appear too (see source_type per file).

Returns {files, count, limit, offset} where each file has: id (the uploaded_file_id create_model needs), filename, original_filename, source_type, row_count, column_count, created_at. Here count IS the true total matching the filter (unlike list_runs, where it is the page length). Column names/dtypes are not in the listing — fetch one upload with get_upload for those.

Args: limit: Page size (API clamps to 1-500; default 50). offset: Rows to skip (paging). name: Optional case-insensitive substring filter on the original filename.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
limitNo
offsetNo
Behavior5/5

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

With no annotations provided, the description carries the full burden, and it delivers: newest-first ordering, inclusion of all source types, true-total count behavior, API clamping of limit, and the explicit absence of column names/dtypes. It also notes the id field's role for create_model, which is valuable behavioral context.

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?

The description is structured and efficient: overview, return shape, caveats, and argument details. Every sentence adds information an agent needs, including the count-vs-page-length warning and the pointer to get_upload for deeper metadata.

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?

The tool has no output schema and no annotations, but the description defines the complete return shape ({files, count, limit, offset}) and every field on each file. It also covers pagination, filtering, and the next-step alternative, making it fully actionable for an agent.

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

Parameters5/5

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

The schema provides only defaults and no descriptions, so there is 0% schema coverage. The description fully compensates by explaining limit with clamping and default, offset as paging, and name as a case-insensitive substring filter on the original filename.

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 opens with a specific verb and resource: 'List the datasets in your workspace (newest first)'. It also distinguishes this tool from siblings by clarifying it covers every source type, not just API uploads, and from get_upload by stating what this listing does and does not include.

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?

The description gives clear context for when this tool is appropriate: listing datasets with pagination and filtering. It explicitly directs the agent to get_upload when column names/dtypes are needed, and it distinguishes the count semantics from list_runs to prevent misuse.

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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