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Glama

Data Austin

by io.github.pipeworx-io

Austin Recent

austin_recent
Read-onlyIdempotent

Recent records from a common Austin, TX open dataset (data.austintexas.gov) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Austin", "Austin 311 requests", "Austin construction permits", "Austin restaurant inspection scores". Names: crime, 311, permits, restaurant_inspections. Returns the latest rows (newest-first). Add a SoQL where to filter; for anything else use austin_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional SoQL filter, e.g. "council_district=9". Omit for all recent rows.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
datasetYesOne of: crime, 311, permits, restaurant_inspections.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "_apiKey": "your-data-austin-api-key",
+    "dataset": "crime"
+  },
+  {
+    "_apiKey": "your-data-austin-api-key",
+    "dataset": "restaurant_inspections",
+    "limit": 50,
+    "where": "score < 85"
+  }
+]
  2. First observed

TDQS

A4.8/5.0
Behavior4/5

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

Annotations already indicate readOnly, idempotent, non-destructive. Description adds that it returns newest-first rows and uses friendly names instead of Socrata IDs. Does not detail pagination or error handling, but overall behavior is transparent enough.

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?

Four concise sentences: purpose, usage preference, dataset names, behavior. Each sentence adds unique value with no redundancy. Effectively front-loaded.

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 4 parameters, full schema coverage, and complete annotations, the description sufficiently covers the tool's functionality. It tells the agent what datasets are available, how to filter, and when to use an alternative. No gaps with 1 required param and no output schema.

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

Parameters5/5

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

Schema coverage is 100%. Description adds meaning by mapping dataset names to real-world categories (crime, 311, etc.) and explains the optional SoQL where filter. It clarifies the friendly name concept and that _apiKey is optional for higher rate limits, going 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 the tool retrieves recent records from Austin open datasets, names the specific datasets, and distinguishes it from austin_query by saying 'for anything else use austin_query'. It is a specific verb+resource with sibling differentiation.

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

Usage Guidelines5/5

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

Explicitly says 'PREFER OVER WEB SEARCH' for common queries like 'recent crime in Austin' and instructs to use austin_query for anything else. Provides clear when-to-use and when-not-to-use guidance.

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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TDQS

A4/5.0
Disambiguation3/5

Many tools have overlapping purposes, such as multiple ask_pipeworx variants (beta, grounded) and several prediction market tools (arbitrage, edges, fill risk, spread). While descriptions help differentiate them, the abundance of similar tools makes it easy for an agent to misselect.

Naming Consistency3/5

Tool names are a mix of snake_case with inconsistent prefixes: some use 'ask_', 'polymarket_', 'austin_', while others are isolated verbs (forget, remember) or compound nouns (entity_profile). The pattern is not uniform but still readable.

Tool Count3/5

With 34 tools, the server is heavy. Although each tool seems justified for its niche, the set could be consolidated (e.g., merging ask_pipeworx variants) to reduce clutter. The count feels slightly excessive for the scope.

Completeness4/5

The server covers a broad range of domains: Austin open data, pipeworx data, prediction markets, memory management, and subscriptions. Core workflows are well-supported, with only minor gaps like a missing cross-source search tool.

Resources