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get_workspace_table

Read-onlyIdempotent

Full workspace table incl. columns (id/name/colType/options/required/order) + rows + cells. A NUMBER cell is a number, null when empty or not a number; every other cell is text as stored. For very large tables prefer paging through list_workspace_rows.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tableId": "<tableId>"
    },
    "name": "get_workspace_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableIdYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already cover readOnly, idempotent, non-destructive, and closed-world behavior, so the description's contribution is the data-format semantics: NUMBER cells are numbers or null, all other cells are text as stored. That is genuinely useful payload context not derivable from the annotations, though it says nothing about payload size limits or behavior on an unknown tableId.

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?

Three compact sentences with zero filler: purpose and return structure first, cell typing next, and the large-table alternative last. The column enumeration is dense but each item carries meaning.

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

Completeness4/5

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

With no output schema, the description appropriately describes the return shape and cell typing itself, which is the main ambiguity for a table-fetch tool. It stops short of covering failure modes or size caps, but nothing essential for a correct call is missing.

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

Parameters3/5

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

There is a single parameter, tableId, with 0% schema description coverage and no explanation in the description. The meaning is inferable from the tool name and required-parameter list, but the description does not compensate for the coverage gap by stating accepted ID format or whether names are accepted.

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?

States a specific verb and resource and then enumerates the exact return shape (columns with id/name/colType/options/required/order, plus rows and cells). It is clearly distinguishable from list_workspace_tables and list_workspace_rows without opening any schema.

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

Usage Guidelines4/5

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

Explicitly names the alternative and the condition that selects it: 'For very large tables prefer paging through list_workspace_rows.' That gives a clear when-not, but there is no guidance on when this tool is preferred over list_workspace_tables or on size thresholds that define 'very large'.

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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