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ThinAirTelematics

ThinAir Data

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suggest_queries

Generate schema-aware query suggestions with ready-to-run SQL to help explore unfamiliar databases and find useful queries.

Instructions

Generate schema-aware query suggestions with ready-to-run SQL. Great for exploring unfamiliar databases or finding useful queries.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • bin/server.js:58-63 (registration)
    Tool registration in the TOOLS array. 'suggest_queries' is registered with a description and empty inputSchema. This is a static stdio adapter that returns a redirect message for all tool calls, so the actual handler logic is not present in this file.
    {
      name: "suggest_queries",
      description:
        "Generate schema-aware query suggestions with ready-to-run SQL. Great for exploring unfamiliar databases or finding useful queries.",
      inputSchema: { type: "object" },
    },
  • Generic handler for all tool calls (including suggest_queries). This local adapter does not execute any tool logic; it returns a static redirect message pointing users to the hosted production server.
    server.setRequestHandler(CallToolRequestSchema, async () => ({
      content: [{ type: "text", text: REDIRECT_MESSAGE }],
      isError: false,
Behavior3/5

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

No annotations are present, so the description must carry the full burden. It mentions 'schema-aware' and 'ready-to-run SQL' but lacks details on authentication, rate limits, or whether suggestions are read-only.

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 with no wasted words. Front-loaded with the core action and followed by a use case.

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 zero parameters and no output schema, the description is largely complete. However, it omits details on return format or prerequisites like an active connection, which could be inferred from sibling tools.

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

Parameters4/5

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

With no parameters and 100% schema coverage, the description adds value by explaining the tool's purpose and output nature, meeting the baseline for a parameterless tool.

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 generates schema-aware query suggestions with ready-to-run SQL, distinguishing it from siblings like 'query_sql' (execution) and 'explain_query' (explanation).

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?

Provides clear context ('Great for exploring unfamiliar databases or finding useful queries') but does not explicitly exclude scenarios or mention alternatives like 'query_history' or 'saved_queries'.

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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