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Glama

Data Oakland

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Oakland Open Data dataset by resource_id (e.g. "ym6k-rx7a"). 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. "ym6k-rx7a" (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": "ym6k-rx7a",
+    "where": "year >= 2020"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations (readOnlyHint, idempotentHint, etc.) already indicate safety; the description adds return format 'Returns matching rows as JSON' and details about SoQL clause formatting, which provides value beyond 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: first states purpose, second adds filtering details. Front-loaded and free of unnecessary words. Every sentence is valuable.

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 7-parameter tool with SoQL syntax, the description covers core usage, return format, and provides an example. It does not explain SoQL itself, but that is domain knowledge; overall sufficient.

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% but the description adds critical nuance: SoQL clauses must be without leading $, illustrated by an example. This enhances understanding beyond schema descriptions.

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', the resource 'Socrata SoQL query against a Oakland Open Data dataset', and the key parameter 'resource_id'. It distinguishes from sibling tools like 'datasets' which lists datasets, not 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 Guidelines4/5

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

The description explicitly lists filtering options (where, select, group, order, limit, offset) and clarifies SoQL syntax (no leading $). It implies usage for querying datasets, though it does not explicitly mention when not to use it or alternatives.

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
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical today, with ask_pipeworx_grounded and deep_research routing through the same 5,756-tool catalog, and validate_claim falling into the same grounded pipeline. The five polymarket_* tools plus bet_research all target prediction-market opportunities with overlapping outputs (edge_pp vs gap_pp vs spread_pp), and compare_entities/entity_profile/recent_changes share the same SEC/XBRL/news fan-out. The verbose descriptions help, but the set itself would frequently misroute an agent.

Naming Consistency3/5

All names are uniformly snake_case with no casing mixing, and the ask_pipeworx_*, polymarket_*, and pipeworx_* prefixes create recognizable families. However, the set mixes verb_noun names (validate_claim, list_subscriptions), bare verbs (query, recall, forget), and noun-phrase names (entity_profile, recent_alerts, datasets, metadata), so there is no single predictable pattern across the server.

Tool Count3/5

34 tools is heavy, but the server's scope is genuinely enormous: it is a gateway to 5,756 tools across 1,504 sources, plus prediction-market analysis, subscriptions, and memory. The count is defensible for that scope, yet several tools (generate_llms_txt, scan_dependency, ai_visibility_check, the memory trio) are peripheral to the core data mission, giving the set a scattershot feel and preventing a well-scoped rating.

Completeness4/5

The core data-research workflow is thoroughly covered: casual lookup (ask_pipeworx), grounded verification (ask_pipeworx_grounded, validate_claim), deep research (deep_research), entity resolution and profiling (resolve_entity, entity_profile, compare_entities, recent_changes), and a six-tool prediction-market suite. Subscriptions and memory have full lifecycles, and Oakland data offers search, schema, and query. Minor gaps exist — no subscription update/pause, no raw dataset export, and no write path for Oakland data — but no advertised workflow hits a dead end.

Resources