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Skeletable

by io.github.eftpmc
OverviewDiscussionsAdmin

export_table

Read-onlyIdempotent

A table as CSV, ready to save as a file or load into a notebook. The first column is each row's name unless names is false. columns picks columns by id, in that order. With proof, each column is followed by its source and quote, and in a table whose proof is optional, by where each value came from. An empty field means no value.

Input Schema

NameRequiredDescriptionDefault
slugYesFrom list_tables
namesNoWhether to start each line with the row's name. Defaults to true
proofNoWhether to add each value's source, quote and origin. Defaults to false
columnsNoColumn ids, from get_table. Leave out for every column
versionNoA saved version's number, from `versions` in get_table. Leave out for the live table

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / version
      Added value: +{
+  "description": "A saved version's number, from `versions` in get_table. Leave out for the live table",
+  "maximum": 9007199254740991,
+  "minimum": 1,
+  "type": "integer"
+}
  2. Added

TDQS

A3.8/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is clear. The description adds useful behavioral detail about output shape: default row names, column ordering, proof mode adding source/quote/origin, and empty fields meaning no value. These details go beyond what annotations provide.

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 compact, front-loaded, and every sentence adds relevant information about the output format or parameter effects. There is no wasted text.

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 does a good job explaining the CSV structure, default name column, column selection, proof mode, and empty field behavior. It could say more about encoding or exact formatting, but it is sufficient for an agent to call the tool correctly.

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?

Schema description coverage is 100%, so the schema already documents all parameters well. The description adds marginal meaning, such as column order and the effect of proof mode on output layout, but much of it restates what the schema provides. A baseline 3 is appropriate when the schema carries most of the semantic load.

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

Purpose4/5

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

The description clearly states that the tool returns a table as CSV, ready to save or load into a notebook, which is a specific output and resource. It does not explicitly distinguish itself from siblings like get_table or list_tables, but the CSV export framing is clear enough for an agent to understand its purpose.

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

Usage Guidelines3/5

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

The description implies usage by saying the output is 'ready to save as a file or load into a notebook,' but it gives no explicit guidance on when to choose this tool over get_table or other siblings. The parameter explanations add context but not selection criteria.

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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