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Data Austin

by io.github.pipeworx-io
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Austin Datasets

austin_datasets
Read-onlyIdempotent

Search the Austin open-data catalogue (data.austintexas.gov) for datasets by keyword. Returns dataset names, descriptions, and Socrata resource ids to use with austin_query.

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": {
      "limit": 20,
      "query": "traffic accidents",
      "_apiKey": "your-data-austin-api-key"
    },
    "name": "austin_datasets"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoMax datasets (1-100, default 20).
queryNoKeyword(s), e.g. "traffic", "water", "budget".
offsetNoOffset for paging.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-data-austin-api-key",
+    "limit": 20,
+    "query": "traffic accidents"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, etc. The description adds context about rate limits via the optional _apiKey parameter and specifies the output fields, which is valuable beyond the annotations. No contradictions.

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 sentences with no filler. The first sentence states purpose clearly; the second specifies return values. All information is relevant and front-loaded.

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 tool with 4 parameters and no output schema, the description covers the core functionality and return values. It could mention pagination or offset, but offset is described in the schema. Overall adequate.

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 100%, so the baseline is 3. The description does not elaborate on parameters beyond what the schema provides, but it adds overall context that the tool is for searching datasets.

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 searches the Austin open-data catalogue by keyword, specifies the return values (dataset names, descriptions, resource ids), and hints at integration with austin_query, distinguishing it from siblings like austin_query and austin_recent.

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 implies usage for finding datasets before querying them via austin_query, providing a clear context. However, it does not explicitly state when not to use this tool or mention alternatives like austin_recent.

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

All Data Austin toolsInspect
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