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add_example

Add a natural-language query and its SQL counterpart to the example store, enabling few-shot learning for accurate SQL generation from user questions.

Instructions

Добавить эталонный пример «NL-запрос → SQL» в Example Store (ТЗ 1.3). Используется для few-shot обучения generate_sql.

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": {
      "query": "<query>",
      "sql": "<sql>"
    },
    "name": "add_example"
  }
}

Input Schema

Name	Required	Description	Default
`sql`	Yes
`query`	Yes
`is_golden`	No
`table_name`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 1, 2026

TDQS

A3.7/5.0

Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It states the tool appends an example, but it does not mention whether duplicate queries are allowed, whether existing examples are replaced, how is_golden affects behavior, or any validation or side effects. The mutation implications are only implicit.

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?

Two concise sentences, front-loaded with the main action and followed by the purpose. Both sentences earn their place, and there is no redundant or filler wording.

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?

The core operation and purpose are clear, and an output schema exists so return values need not be described. However, the tool has four parameters with no schema descriptions and no annotations, and the description leaves the optional parameters' semantics and behavioral edge cases unexplained. It is adequate for a simple call but not fully complete.

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 description coverage is 0%, but the phrase 'NL-запрос → SQL' gives meaningful semantics for query and sql, the two required parameters. However, is_golden and table_name are left entirely to inference, and their defaults and roles are not explained. The description partially compensates for the schema, but not fully.

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 a specific verb and resource: it adds a reference example (NL-query → SQL) to the Example Store, and explicitly ties this to few-shot training for generate_sql. This distinguishes it from sibling tools like sql_query or generate_sql, which consume rather than add examples.

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 provides clear context by stating the tool is used for few-shot training of generate_sql, which implies when an agent should add an example. It does not explicitly state when not to use it or name alternatives, but the intended use is concrete enough for routing.

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