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Glama

Texas Open Data

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Texas Open Data dataset by resource_id (e.g. "54pj-3dxy"). Filter with where/select/group/order (SoQL clauses, without the leading $) plus limit/offset. Returns matching rows as JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupNoSoQL $group column(s).
limitNoMax rows (default Socrata 1000).
orderNoSoQL $order, e.g. "date DESC".
whereNoSoQL $where filter, e.g. "year >= 2020 AND status = 'Active'".
offsetNoPagination offset.
selectNoSoQL $select, e.g. "name, count(*) AS n".
resource_idYesDataset id, e.g. "54pj-3dxy" (from datasets).

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: +[
+  {
+    "limit": 100,
+    "resource_id": "54pj-3dxy",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "name",
+    "order": "total DESC",
+    "resource_id": "54pj-3dxy",
+    "select": "name, count(*) AS total"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, so the tool is safe for reads. The description adds that it returns matching rows as JSON and clarifies SoQL clause syntax (without leading $). This provides behavioral context beyond annotations without contradicting them.

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 two sentences long, front-loads the core action, and provides essential details about input format and output. Every word earns its place with no redundancy.

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 query tool with 7 parameters all documented in the schema, the description sufficiently explains the purpose and output format. It could benefit from mentioning default limit behavior or error handling, but given the annotations and schema, it is complete enough for an agent to invoke correctly.

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?

Schema coverage is 100%, so the descriptions of each parameter exist. The description adds value by explaining how to use SoQL clauses without the '$' prefix and provides examples in the schema's examples field. This clarifies usage beyond the raw 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 runs Socrata SoQL queries against Texas Open Data datasets using a resource_id. It specifies the SoQL clauses and parameters, and distinguishes itself from sibling tools like 'datasets' which likely list datasets rather than query them.

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

Usage Guidelines2/5

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

The description does not provide guidance on when to use this tool versus sibling tools. No explicit when-to-use or when-not-to-use instructions are given, leaving the agent to infer use cases from the tool's name and context.

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

A3.8/5.0
Disambiguation3/5

Tools are individually well-described, but there is overlapping functionality among ask_pipeworx, ask_pipeworx_grounded, and deep_research, as well as multiple prediction market tools. The detailed descriptions help, but the number of similar tools creates some ambiguity.

Naming Consistency4/5

Tool names follow a consistent snake_case pattern with descriptive prefixes (e.g., ask_pipeworx, entity_profile, polymarket_edges). There are minor deviations like 'deep_research' vs 'bet_research' but overall the naming is predictable and clear.

Tool Count2/5

The server is named 'Texas Open Data' but only 3 of 33 tools (datasets, metadata, query) are directly related to Texas open data. The remaining tools cover a much broader domain (Pipeworx ecosystem), making the tool count inappropriate for the stated purpose.

Completeness2/5

For the stated purpose of Texas Open Data, the tool surface is incomplete—only basic query and metadata capabilities are provided, lacking data management, update, or delete operations. As a general pipeworx server it might be more complete, but the name suggests Texas-specific data.

Resources