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RawTree MCP Server

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Hybrid

List Tables

list-tables

Retrieve all tables in a RawTree database with row and byte counts to discover available data, find table names for queries, or verify table creation.

Instructions

Purpose: List all tables in a RawTree database with row and byte counts.

NOT for: Reading table rows. Use run-query for data and describe-table for columns.

Returns: Tables plus database and organization context.

When to use:

  • User asks what data exists

  • You need a table name before querying

  • You want to verify that an insert auto-created a table

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
clusterNoRawTree cluster to use for this operation.
databaseNoOptional RawTree database override. RawTree uses the default database when omitted.
organizationNoRawTree organization containing the target cluster.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed4 schema fields changedv0.3.2
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • addedInput schema / properties / cluster
      Added value: +{
+  "description": "RawTree cluster to use for this operation.",
+  "minLength": 1,
+  "type": "string"
+}
    • addedInput schema / properties / database
      Added value: +{
+  "description": "Optional RawTree database override. RawTree uses the default database when omitted.",
+  "minLength": 1,
+  "type": "string"
+}
    • addedInput schema / properties / organization
      Added value: +{
+  "description": "RawTree organization containing the target cluster.",
+  "minLength": 1,
+  "type": "string"
+}
  2. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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 (tables with row/byte counts plus database and organization context), which is real behavioral context. It stops short of stating auth requirements, pagination, or behavior on a missing/ambiguous database, leaving those gaps.

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?

Front-loaded purpose statement, then compact labelled sections (NOT for / Returns / When to use). Every sentence carries routing or scope information with no filler; the header scaffolding aids scanning rather than 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 read-only list tool with no output schema and no annotations, the description supplies what the returns look like and how it differs from siblings. It is nearly complete; only edge-case behavior (empty database, permission failures) is unaddressed, which is minor for this tool class.

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 schema already documents cluster, database, and organization, including which is optional. The description references database and organization context but adds no syntax, format, or defaulting detail beyond the schema. Baseline 3 applies when the schema does the heavy lifting.

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+resource (list tables in a RawTree database) and immediately scopes what it returns (row and byte counts). It explicitly distinguishes itself from siblings run-query and describe-table, so an agent can route correctly 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 Guidelines5/5

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

The 'NOT for' section names alternatives (run-query for data, describe-table for columns) with the condition that selects them, and the 'When to use' bullets give three concrete triggering scenarios. Both when-to-use and when-not-to-use are explicit.

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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