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Glama

Data Orlando

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Orlando Open Data dataset by resource_id (e.g. "ryhf-m453"). 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. "ryhf-m453" (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: +[
+  {
+    "resource_id": "ryhf-m453",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "category",
+    "limit": 50,
+    "order": "n DESC",
+    "resource_id": "ryhf-m453",
+    "select": "name, count(*) AS n"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior4/5

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

The description adds value beyond annotations by specifying the SoQL syntax (clauses without leading $), return format (JSON), and default limit (1000). No contradictions 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?

Two sentences with front-loaded verb and resource. Every sentence adds value; 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?

Given the lack of output schema, the description appropriately mentions JSON return format. It covers key usage aspects but could mention error behavior or pagination details.

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%, so the baseline is 3. The description reinforces parameter usage with examples but does not add significant new meaning beyond what the schema already provides.

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 verb 'run a Socrata SoQL query' against a specific resource (Orlando Open Data dataset by resource_id). It distinguishes from sibling tools like 'datasets' which list datasets.

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?

The description does not explicitly state when to use this tool versus alternatives or provide conditions for use. It implies usage but lacks guidance on excluding other tools.

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

A3.9/5.0
Disambiguation3/5

Several clusters overlap in purpose: ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded / deep_research all answer factual questions, and the six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) have subtle boundaries even with detailed descriptions. The descriptions are strong, but an agent must read carefully to reliably pick the right tool.

Naming Consistency4/5

Most tools follow a verb_noun snake_case pattern (ask_pipeworx, compare_entities, resolve_entity, validate_claim) with recognizable family prefixes (polymarket_*, pipeworx_*, ask_pipeworx_*). Minor deviations like single-noun names (datasets, metadata, query) and mixed lookup verbs (ask vs query vs search vs discover) keep it from a 5.

Tool Count2/5

34 tools is heavy for a single server, and the scope sprawls across general data routing, prediction-market analytics, memory management, subscription bookkeeping, AI visibility checks, and npm dependency scanning. The families are organized, but the count exceeds the 25-tool threshold and would be better split into focused servers.

Completeness4/5

For a data-research server, coverage is broad: universal routing, grounded answers, deep research, entity profiles, comparisons, change feeds, claim verification, dataset search/query/metadata, subscriptions, and memory. Minor gaps exist—no subscription update operation and no full-catalog browse beyond search—but agents can work around them.

Resources