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local_ydb_generate_schema

Read-onlyIdempotent

Generates YDB table DDL (CREATE, ALTER, DROP, indexes) from structured JSON specs, returning a validated script with references and warnings without applying changes.

Instructions

Read-only structured YDB table DDL generator. It renders strict JSON specs for CREATE TABLE, ALTER TABLE, DROP TABLE, and secondary indexes, returns the generated script with official references and warnings, and can optionally validate through the YDB JS SDK without applying changes.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
profileNoNamed profile from local-ydb.config.json. Defaults to config.defaultProfile.
configPathNoExplicit local-ydb config file path to load for this tool call. Useful when the MCP server should pick up a different config without restart.
databasePathNoYDB database path to use when validate=true. Defaults to the configured tenant root.
validateNoIf true, validate the generated DDL through local_ydb_apply_schema action=validate. This tool never applies DDL.
statementsYesStructured schema statement specs to render into YDB table DDL.
timeoutMsNoSDK validation timeout in milliseconds when validate=true. Defaults to 120000.
maxOutputBytesNoMaximum UTF-8 bytes returned per validation issue stream when validate=true. Defaults to 65536.
Behavior4/5

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

Annotations already declare readOnlyHint, destructiveHint, idlempotentHint, and openWorldHint. The description adds value by specifying the return includes references and warnings, and that validation is optional and safe. It reinforces the read-only nature 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 three sentences long, front-loaded with the core purpose, and every sentence adds essential information. No redundancy or fluff.

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?

Given the tool's complexity (multiple statement types and validation), the description provides a good overview. However, the output format (generated script with references/warnings) is not elaborated, and without an output schema, the agent could benefit from more detail on the return structure.

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% with detailed descriptions for all 7 parameters. The tool description does not add parameter-specific insight beyond what the schema already provides, meeting the baseline for high coverage.

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 clearly states the tool is a read-only DDL generator for YDB tables, listing specific operations (CREATE, ALTER, DROP, indexes) and distinguishing from the apply tool by explicitly noting it never applies DDL. This differentiates it from sibling tools like local_ydb_apply_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?

The description implies usage for generating and optionally validating DDL without applying changes, and mentions validation via local_ydb_apply_schema. However, it lacks explicit when-not-to-use or alternative scenarios, relying on context from sibling tool names.

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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