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The OtterStax MCP Server

The OtterStax MCP Server

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by otterstax
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describe_table

Retrieve column names, SQL data types, and nullability for a specified table so AI agents can inspect schema details before querying federated data.

Instructions

Columns of a table with their SQL types and nullability.

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": {
      "table": "<table>"
    },
    "name": "describe_table"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYesFull table name from list_tables, e.g. sales.ops.orders

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.0.0

TDQS

B3.4/5.0
Behavior3/5

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

With no annotations, the description carries the full burden, and it does disclose the return payload (column names, SQL types, nullability), which implicitly communicates a read-only metadata operation. It says nothing about behavior on a missing table, permission requirements, or output volume, so the disclosure is partial.

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?

A single short sentence that front-loads the essential information with zero filler. Nothing in it is redundant or padding.

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?

For a simple one-parameter metadata read with no output schema, the description adequately explains what comes back (columns, types, nullability). It could be more complete by noting that all columns are returned or that it errors on an unknown table, but the core need is covered.

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 coverage is 100%, so the single 'table' parameter is already fully documented in the schema, including the 'sales.ops.orders' naming convention. The description adds no syntax or format meaning beyond what structured data provides, which is the baseline case for a well-covered schema.

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?

Names the resource (a table) and precisely what is returned — columns with SQL types and nullability — which distinguishes it from siblings like table_stats or list_tables. However, it uses a noun phrase with no verb and never explicitly contrasts itself with the other metadata tools.

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

Usage Guidelines2/5

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

There is no when-to-use guidance, no prerequisite stated (the fact that the table name should come from list_tables lives only in the schema), and no mention of alternatives such as table_stats for row-level metrics. The agent must infer usage entirely.

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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