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MySQL MCP Server

MySQL JSON Stats

mysql_json_stats
Read-only

Analyze JSON column statistics including depth, size, and key frequency in MySQL tables.

Instructions

Analyze statistics for a JSON column including depth, size, and key frequency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
colNoAlias for column
sqlNoAlias for where
nameNoAlias for table
queryNoAlias for where
rowIdNoAlias for where (used with idColumn)
tableNoTable name
whereNoOptional WHERE clause
columnNoJSON column name
filterNoAlias for where
idColumnNoAlias for where (used with rowId)
tableNameNoAlias for table
sampleSizeNoSample size for statistics

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeNoError code (e.g. VALIDATION_ERROR, QUERY_ERROR)
dataNo
errorNoError message if operation failed
detailsNoAdditional error context
metricsNoToken estimation metrics
successYesWhether the operation succeeded
categoryNoError category (validation, query, connection, internal)
suggestionNoSuggested fix for the error
recoverableNoWhether the error is recoverable
Behavior4/5

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

Annotations declare readOnlyHint=true and destructiveHint=false. Description adds that it analyzes depth, size, and key frequency, which is useful behavioral context beyond annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence, concise and front-loaded with the tool's purpose. However, could be improved by structuring information more clearly.

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

Completeness3/5

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

With 12 parameters (many aliases) and no required params, the description should explain parameter usage. Output schema exists, so return values are covered, but parameter selection is inadequately addressed.

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

Parameters2/5

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

Schema description coverage is 100%, but many parameters are aliases (e.g., col, sql, name) with identical descriptions. The tool description adds no clarity on which parameter to use or how to avoid ambiguity.

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 verb 'analyze' and the resource 'JSON column', listing specific statistics (depth, size, key frequency). It distinguishes from many JSON manipulation sibling 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?

No guidance on when to use this tool versus alternatives. Does not mention when not to use or what scenarios are appropriate for this analysis tool.

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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