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ThinAirTelematics

ThinAir Data

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watch_table

Track changes in a database table by monitoring row count and latest record. Compare snapshots to detect differences, with built-in scheduling for automatic checks.

Instructions

Monitor a table's row count and latest record. Compares to previous snapshot to show changes. Built-in scheduler. [ARCHITECT tier]

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Implementation Reference

  • Schema/registration entry for the 'watch_table' tool in the static tool catalog. This is a stub definition with no input schema (type: object) and a description. The actual execution is not handled locally — this file is a local reference adapter that redirects to the hosted server at https://data.thinair.co/mcp.
    {
      name: "watch_table",
      description:
        "Monitor a table's row count and latest record. Compares to previous snapshot to show changes. Built-in scheduler. [ARCHITECT tier]",
      inputSchema: { type: "object" },
  • bin/server.js:188-188 (registration)
    Registration of the watch_table tool via ListToolsRequestSchema handler. The tool is part of the TOOLS array returned when the client lists tools.
    server.setRequestHandler(ListToolsRequestSchema, async () => ({ tools: TOOLS }));
  • CallToolRequestSchema handler — all tool calls (including watch_table) get a redirect message to the hosted MCP server. No actual watch_table logic exists in this local adapter.
    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 provided, so description carries full burden. It explains monitoring and comparison behavior, but lacks details on permissions, side effects, or data retention.

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 focused sentences plus a tier tag. Front-loads the core purpose efficiently.

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?

For a zero-parameter tool with no output schema, the description covers the essential functionality. Could mention output format or how to manage the scheduler, but overall adequate.

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?

Zero parameters, baseline 4. Description adds no parameter info since none exist, but it's consistent.

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?

Description clearly states the tool monitors row count and latest record, comparing to previous snapshot. This distinguishes it from sibling tools like query_sql or analyze_table, as no other tool provides this time-series monitoring functionality.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Implies usage for ongoing monitoring with a built-in scheduler, but does not explicitly state when to use versus alternatives like detect_anomalies or when not to use.

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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