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Glama

Data Cincinnati

by io.github.pipeworx-io

Cincinnati Recent

cincinnati_recent
Read-onlyIdempotent

Recent records from a common Cincinnati open dataset (data.cincinnati-oh.gov) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Cincinnati", "Cincinnati 311 requests", "Cincinnati building permits". Names: 311, crime, permits. Returns the latest rows (newest-first). Add a SoQL where to filter; for anything else use cincinnati_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional SoQL filter. Omit for all recent rows.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
datasetYesOne of: 311, crime, permits.

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",
+    "dataset": "crime"
+  },
+  {
+    "_apiKey": "your-data-cincinnati-api-key",
+    "dataset": "311",
+    "limit": 50,
+    "where": "created_date > '2024-01-01'"
+  }
+]
  2. First observed

TDQS

A4.6/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=true, idempotentHint=true, etc. The description adds context about returning newest-first and SoQL filtering, which aligns with and extends the annotation information.

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?

Three concise sentences front-load the purpose, provide usage guidance, and clarify parameters, with 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?

The description covers the tool's purpose, inputs, and behavior (newest-first), but lacks details on output structure; however, for a simple records tool, this is 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%, so baseline is 3. The description adds value by clarifying that _apiKey is optional for higher rate limits and that dataset uses friendly names, 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 tool retrieves recent records from a Cincinnati open dataset using friendly names, distinguishing it from the sibling cincinnati_query tool.

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 recommends using this tool over web search for specific queries and directs users to cincinnati_query for anything else, providing clear when-to-use and when-not-to-use guidance.

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