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Glama

Data Vermont

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Vermont Open Data dataset by resource_id (e.g. "jgqy-2smf"). 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. "jgqy-2smf" (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": "jgqy-2smf",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "name",
+    "order": "total DESC",
+    "resource_id": "jgqy-2smf",
+    "select": "name, count(*) AS total"
+  }
+]
  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, idempotentHint, etc., so the description adds value by clarifying that SoQL clauses should omit the leading $ and that results are returned as JSON. No contradiction with 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?

The description is two sentences, no filler. It packs purpose, resource, parameter usage, and output format efficiently.

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 fully documented in schema, the description covers key aspects: what the tool does, how to use parameters, and return format. Minor omission: no mention of default limit (covered in schema) or detailed pagination hints.

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 individual parameter descriptions. The tool description adds context about the SoQL syntax (no leading $) and provides examples that combine parameters, enhancing understanding 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 action (run a SoQL query) and the resource (Vermont Open Data dataset by resource_id). It distinguishes itself from sibling tools by specifying the target and query language, which no other sibling explicitly does.

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 explains how to use parameters (e.g., SoQL clauses without leading $, limit/offset) and provides examples. However, it does not explicitly advise when to use this tool versus alternatives like 'datasets' or 'search_within', leaving some ambiguity.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx trio (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket cluster (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) could cause confusion without careful reading. However, the detailed descriptions effectively differentiate each tool's specific role.

Naming Consistency5/5

All tool names follow consistent snake_case convention with a verb-noun pattern (e.g., compare_entities, recent_changes, resolve_entity). There are no mixed conventions or chaotic naming, making the set predictable and easy to navigate.

Tool Count4/5

At 34 tools, the set is extensive but justified by the server's broad scope covering Vermont open data, Pipeworx data platform, prediction markets, and utility features. Each tool earns its place, though the count is slightly high compared to typical servers.

Completeness4/5

The tool surface covers data retrieval, entity analysis, prediction markets, memory, subscriptions, feedback, and claim validation. Minor gaps exist (e.g., no direct compliance tools), but the set is comprehensive for its stated purpose of data integration and analysis.

Resources