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Glama

Data Cincinnati

by io.github.pipeworx-io

Cincinnati Query

cincinnati_query
Read-onlyIdempotent

Run a raw SoQL query against any Cincinnati open-data resource (data.cincinnati-oh.gov) by its Socrata id (8-char like "k59e-2pvf"). Full SoQL: where/select/group/order/limit/offset. Use cincinnati_datasets to find a resource id, or cincinnati_recent for the common ones.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupNoSoQL $group.
limitNoMax rows (default 100, max 5000).
orderNoSoQL $order.
whereNoSoQL $where filter.
offsetNoRow offset for paging.
selectNoSoQL $select.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
resource_idYesSocrata resource id, e.g. "k59e-2pvf".

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: +[
+  {
+    "_apiKey": "your-data-cincinnati-api-key",
+    "group": "offense_type",
+    "resource_id": "k59e-2pvf",
+    "select": "count(*)"
+  },
+  {
+    "_apiKey": "your-data-cincinnati-api-key",
+    "limit": 100,
+    "order": "reported_date DESC",
+    "resource_id": "k59e-2pvf",
+    "where": "reported_date >= '2024-01-01'"
+  }
+]
  2. First observed

TDQS

A4.7/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is clear. The description adds optional _apiKey context for rate limits, which is useful beyond annotations. No contradictions.

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, front-loaded with the primary action, no wasted words. Efficiently communicates all necessary information without verbosity.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a query tool with comprehensive annotations and 100% schema description coverage, the description covers all essential aspects: data source, resource ID format, query clauses, and key parameter nuances. No output schema needed as per rule.

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 baseline 3. The description goes beyond by listing SoQL clauses (where/select/group/order/limit/offset) and explaining the _apiKey parameter's purpose for higher rate limits, adding value over 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?

Description clearly states the tool runs a raw SoQL query against Cincinnati open-data resources, specifies the data source URL and the 8-character resource ID format, and distinguishes it from sibling tools cincinnati_datasets and cincinnati_recent by mentioning their respective uses.

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

Usage Guidelines5/5

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

Explicitly provides guidance on when to use cincinnati_datasets to find a resource ID or cincinnati_recent for common ones, and by implication when to use this tool directly for raw queries, satisfying the agent's decision-making needs.

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
Disambiguation2/5

Several tool clusters have unclear boundaries: ask_pipeworx, ask_pipeworx_beta (explicitly identical), ask_pipeworx_grounded, and deep_research all route the same queries, and the five polymarket_* tools overlap in opportunity scanning. entity_profile, compare_entities, and recent_changes also pull similar company data, making tool selection genuinely ambiguous.

Naming Consistency4/5

All tool names are snake_case with a mostly verb-first convention (ask_pipeworx, scan_dependency, validate_claim, resolve_entity). A few noun-first names like polymarket_edges, entity_profile, and recent_alerts deviate slightly, but the pattern is predictable and readable throughout the 34-tool set.

Tool Count2/5

At 34 tools, the surface is heavy for what the server name (Data Cincinnati) implies, and only 3 tools actually relate to Cincinnati open data. The rest spans prediction markets, npm analysis, AI visibility, memory, and subscriptions, suggesting either scope creep or a misleading server name.

Completeness3/5

The research workflow is fairly covered: discovery (discover_tools, suggest_questions), query (ask_pipeworx), grounding (ask_pipeworx_grounded), verification (validate_claim), profiling (entity_profile), comparison (compare_entities), and monitoring (subscribe, recent_changes). However, notable gaps exist — no direct single-source raw query, no export/visualization, no subscription or alert management details beyond basic CRUD, and the Cincinnati-specific surface is thin (no geospatial or full-catalog access).

Resources