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Glama

Washington State Open Data

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Washington State Open Data dataset by resource_id (e.g. "qxh8-f4bd"). 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. "qxh8-f4bd" (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": "qxh8-f4bd",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "name",
+    "order": "total DESC",
+    "resource_id": "qxh8-f4bd",
+    "select": "name, count(*) AS total"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare it as a read-only, idempotent operation. Description adds that results are returned as JSON and SoQL clauses omit the leading $, but does not disclose any behavioral traits beyond annotations (e.g., pagination, rate limits).

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?

Single sentence packed with essential information: action (run SoQL query), target (dataset by resource_id), optional parameters (SoQL clauses plus limit/offset), and output format (JSON). No wasted words.

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?

Adequately covers the tool's purpose and parameters given its moderate complexity. However, it does not describe the return structure beyond 'matching rows as JSON' or mention error cases, which would improve completeness.

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% with descriptions for all parameters. Description adds valuable context: SoQL clauses should not include leading $, and provides examples that illustrate parameter usage, 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?

Clearly states it runs a Socrata SoQL query against a specific open data portal (Washington State Open Data) by resource_id. Differentiates from siblings by specifying a unique data source and query language.

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?

Indicates usage for querying datasets with SoQL clauses, but does not explicitly state when to use this tool versus alternatives (e.g., datasets or search_within). Lacks guidance on when not to use it.

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

All Washington State Open Data toolsInspect
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TDQS

A3.6/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions, but there is overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, all serving data retrieval. Also, prediction market tools (e.g., bet_research, polymarket_arbitrage) are closely related, causing potential ambiguity.

Naming Consistency2/5

Naming conventions are inconsistent: mostly snake_case (ask_pipeworx, entity_profile) but includes camelCase (ai_visibility_check). No clear pattern, mixing verb_noun and other structures.

Tool Count1/5

33 tools is excessive for a server named 'Washington State Open Data'; only 3 tools (datasets, metadata, query) are directly relevant, while the rest are unrelated Pipeworx/Polymarket tools. The count does not match the server's stated scope.

Completeness1/5

The tool set severely misaligns with the server name: it covers general data and prediction markets rather than Washington State Open Data. Only basic query and metadata tools exist for the stated domain, leaving many common dataset operations (e.g., CRUD) missing and providing entirely irrelevant functionality.

Resources