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Data Cincinnati

by io.github.pipeworx-io

Cincinnati Datasets

cincinnati_datasets
Read-onlyIdempotent

Search the Cincinnati open-data catalogue (data.cincinnati-oh.gov) for datasets by keyword. Returns dataset names, descriptions, and Socrata resource ids to use with cincinnati_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax datasets (1-100, default 20).
queryNoKeyword(s).
offsetNoOffset for paging.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.

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",
+    "query": "crime data"
+  },
+  {
+    "_apiKey": "your-data-cincinnati-api-key",
+    "limit": 10,
+    "query": "building permits"
+  }
+]
  2. First observed

TDQS

A4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, providing high transparency. The description adds context about the source system and return fields, which is useful but not critical 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 with no waste. Every word adds value: specifies catalogue, keyword search, return types, and relationship to cincinnati_query. Front-loaded with the core action.

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?

No output schema exists, but the description explicitly states what is returned (names, descriptions, resource IDs). It omits pagination behavior and error conditions, yet given the schema coverage and annotations, the description is largely sufficient for a straightforward search tool.

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 description coverage is 100%, with clear parameter descriptions for limit, query, offset, and apiKey. The tool description does not add additional meaning beyond what the schema already provides, so baseline 3 is appropriate.

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 searches the Cincinnati open-data catalogue by keyword and returns dataset names, descriptions, and Socrata resource IDs. It references specific resources (data.cincinnati-oh.gov) and distinguishes from sibling tools like cincinnati_query and cincinnati_recent by mentioning the return of resource ids for use with cincinnati_query.

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 when to use the tool (search for datasets by keyword) and how output relates to cincinnati_query. It does not explicitly state when not to use or list alternatives beyond the implied workflow, but the context is clear enough for an agent to decide.

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