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Cbs Nl

by io.github.pipeworx-io
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Table Info

table_info
Read-onlyIdempotent

Metadata for one table: title, summary, time period covered, frequency, update status and description. Pass a table id like "37296eng".

Example tool call

This call uses example arguments supplied in the tool’s input schema.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "table": "37296eng"
    },
    "name": "table_info"
  }
}

Input Schema

NameRequiredDescriptionDefault
tableYesCBS table id, e.g. "37296eng".

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "table": "37296eng"
+  }
+]
  2. First observed

TDQS

A4.1/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is covered. The description adds the specific metadata fields returned, which is useful, but it does not describe any other behavioral aspects like error handling, rate limits, or response format beyond the fields listed.

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?

The description is a single sentence that front-loads the purpose ('Metadata for one table') and lists the output fields compactly. It also includes the required usage example without unnecessary words. Every element earns its place.

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

Completeness5/5

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

Given the tool's simplicity (one parameter, read-only annotations, no output schema), the description sufficiently covers what the tool does and what it returns. The listed metadata fields set agent expectations, and no additional details are critical for successful invocation.

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?

The schema has 100% coverage with a description for the 'table' parameter, so the baseline is 3. The description reinforces this with an explicit example ('37296eng'), making the expected input format immediately clear to the agent, adding value beyond the schema.

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 that the tool returns metadata for a single table, listing specific metadata fields (title, summary, time period, frequency, update status, description). This distinguishes it from sibling tools like search_tables (which find tables) and get_data (which retrieves data), and it includes a concrete example ID.

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?

The description gives an instruction to pass a table ID, which implies when to use it (when you need metadata for a known table). However, it does not explicitly compare to alternatives or state exclusions, so the usage guidance is clear but not fully explicit.

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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