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smart_summary

Summarize any database table automatically, extracting key metrics, top groups, anomalies, and patterns. Optionally focus on a specific aspect for targeted insights.

Instructions

Умное саммари: ключевые метрики, топ-группы, аномалии, паттерны.

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_name": "<table_name>"
    },
    "name": "smart_summary"
  }
}

Input Schema

Name	Required	Description	Default
`focus`	No
`table_name`	Yes

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

C2.2/5.0

Behavior2/5

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

No annotations are provided, so the description must carry the full burden of behavioral disclosure. It states what kinds of insights the summary includes, but it does not disclose how the summary is computed, whether it is read-only, what scope it covers, or any limitations or side effects.

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

Conciseness3/5

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

The description is short and contains no fluff, but it is more of a fragment than a structured explanation. It is concise without being informative enough to be considered well-structured.

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

Completeness2/5

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

Although an output schema exists, so return-value documentation is less critical, the description still fails to explain the meaning of the 'focus' parameter or how this tool relates to the many analytical sibling tools. For a parameterized tool with no annotations, this is not complete.

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

Parameters1/5

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

Schema description coverage is 0%, and the description adds no meaning to either parameter. 'table_name' is only implicit from the tool's purpose, and 'focus' is entirely unexplained, even though it likely controls what the summary emphasizes.

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

Purpose3/5

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

The description names the tool's function as a 'smart summary' and lists its contents (key metrics, top groups, anomalies, patterns), so an agent can infer it produces a synthesized analytical overview. However, it is a noun phrase rather than a specific verb+resource statement, and it does not distinguish this from closely related siblings like auto_insights, data_story, or detect_patterns.

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?

The description implies the tool is used to get a concise analytical summary of a table, but it gives no explicit guidance on when to choose it over alternatives or when not to use it. No exclusions, prerequisites, or routing hints are provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.