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Data Nola

by io.github.pipeworx-io

Nola Recent

nola_recent
Read-onlyIdempotent

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

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds context that results are returned newest-first and supports optional filtering via SoQL where clause. It does not contradict annotations and provides useful behavioral details beyond what annotations offer.

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?

Extremely concise: two sentences plus a list of dataset names. The core purpose is front-loaded, followed by usage preference and parameter hints. No wasted words; every sentence adds value.

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 no output schema, the description explains the return order (newest-first) and the ability to limit and filter. However, it does not mention that the column structure may vary per dataset, which could be useful. Overall, it is fairly complete for a simple data retrieval tool.

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 naming the allowed dataset values (311, crime, permits) and explaining that the 'where' parameter is a SoQL filter. Examples further clarify usage, going beyond the schema's property 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 explicitly states it retrieves recent records from common New Orleans open datasets using friendly names, avoiding the need for a Socrata identifier. It distinguishes itself from the sibling tool 'nola_query' by specifying this tool is for simple recent data retrieval.

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?

Provides explicit guidance to prefer this tool over web search for specific queries like 'recent crime in New Orleans', and directs users to use 'nola_query' for more complex needs. This clearly differentiates when to use this tool versus 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 boundaries blur: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve overlapping query/research entry points, and ask_pipeworx_beta is currently identical to ask_pipeworx by the server's own description. entity_profile, recent_changes, and compare_entities also cover overlapping company-investigation territory, forcing agents to parse long descriptions to avoid mis-selection.

Naming Consistency4/5

Names are uniformly snake_case and mostly follow a predictable verb-object or domain-prefix pattern (ask_pipeworx_*, polymarket_*, nola_*, subscribe/unsubscribe). Minor deviations like nola_datasets, polymarket_edges, and ai_visibility_check are noun-first rather than verb-first, but the overall convention is still readable and coherent.

Tool Count2/5

34 tools is well above the 15-25 heavy range and includes multiple near-duplicate query modes, four closely related Polymarket analysis tools, and memory/subscription utilities layered on top of the core data-access surface. While the server appears to be a broad data platform, the count is bloated for an agent to navigate efficiently.

Completeness4/5

The tool surface is remarkably broad: discovery, single-answer lookup, grounded verification, deep research, entity resolution, entity profiles, comparisons, claim validation, NOLA querying, prediction-market analytics, memory, subscriptions, and feedback are all covered with few obvious dead ends. The main gap is that the NOLA-specific surface is thin relative to the server name, though nola_query plus nola_datasets provides a flexible escape hatch.

Resources