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Glama

Data Dallas

by io.github.pipeworx-io

Dallas Recent

dallas_recent
Read-onlyIdempotent

Recent records from a common Dallas open dataset (www.dallasopendata.com) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Dallas", "Dallas 311 requests", "Dallas building permits". Names: 311, crime, permits. Returns the latest rows (newest-first). Add a SoQL where to filter; for anything else use dallas_query.

Input Schema

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

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-dallas-api-key",
+    "dataset": "crime"
+  },
+  {
+    "_apiKey": "your-data-dallas-api-key",
+    "dataset": "311",
+    "limit": 50,
+    "where": "created_date > '2024-01-01'"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. Description adds that records are returned newest-first and supports SoQL where filtering, which are useful behavioral details beyond the annotations.

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?

Description is extremely concise—five sentences with no wasted words. Front-loaded with purpose, then usage guidance, then parameter hints. Every sentence adds value.

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?

Covers purpose, usage, parameter hints, and relationship to sibling tools. Without an output schema, the description could mention return fields, but for a simple recent-records tool this is sufficient.

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 coverage is 100% with descriptions for all four parameters. Description restates dataset enum values and default limit, but does not add significant new meaning. Baseline 3 is appropriate as schema does the heavy lifting.

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 it retrieves recent records from common Dallas open datasets by friendly name, avoiding the need for Socrata IDs. It distinguishes from sibling tools like dallas_query and web search by specifying its scope.

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 advises preference over web search for specific queries (recent crime, 311, building permits) and directs to use dallas_query for anything else. Provides clear context for when to use this tool.

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
Disambiguation4/5

Most tools have clearly distinct purposes, with a few overlapping pairs (e.g., ask_pipeworx vs ask_pipeworx_grounded, multiple polymarket tools) that could cause mild confusion, but descriptions adequately differentiate them.

Naming Consistency3/5

Tool names consistently use snake_case, but the verb_noun pattern is not consistently applied; some names are noun_noun (dallas_datasets, bet_research) or adjective_noun (ai_visibility_check), creating a mixed nomenclature.

Tool Count2/5

With 33 tools, the server exceeds the typical well-scoped range. While the broad data domain justifies many tools, the count feels heavy and would benefit from consolidation of related functions.

Completeness4/5

The tool set covers a wide array of domains (company data, drugs, economics, prediction markets, memory, subscriptions) with only minor gaps (e.g., no direct web search tool, as ask_pipeworx mostly covers it). Overall, it is comprehensive for its purpose.

Resources