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Data La

by io.github.pipeworx-io

La Recent

la_recent
Read-onlyIdempotent

Recent records from a common Los Angeles open dataset (data.lacity.org) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Los Angeles / LA", "LAPD arrests", "LA building permits", "MyLA311 requests", "active businesses in LA". Names: crime, arrests, businesses, permits, 311. Returns the latest rows (newest-first). Add a SoQL where to filter; for anything else use la_query. (Note: LA 311 is partitioned by year — the shortcut points at the latest year; use la_datasets for other years.)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional SoQL filter, e.g. "crm_cd_desc='BURGLARY'" or "area_name='Hollywood'". Omit for all recent rows.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
datasetYesOne of: crime, arrests, businesses, permits, 311.

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-la-api-key",
+    "dataset": "crime"
+  },
+  {
+    "_apiKey": "your-data-la-api-key",
+    "dataset": "arrests",
+    "limit": 50,
+    "where": "area_name='Hollywood'"
+  }
+]
  2. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, openWorldHint=true. The description adds useful context: returns rows newest-first, supports SoQL filtering, and notes the 311 partition limitation. No contradictions with 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?

The description is concise, comprising two main sentences and a parenthetical note. It is front-loaded with the essential purpose and every sentence adds value. No wasted words or redundancy.

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 tool with 4 parameters, no output schema, and simple behavior, the description adequately covers what the tool does, how to use it, and its limitations (e.g., 311 partition). It briefly mentions optional apiKey for rate limits. Could hint at pagination, but the limit parameter suffices.

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 coverage is 100% with descriptions for all parameters. The description adds context about the SoQL where filter and justifies the dataset enum names, but does not significantly surpass the schema's own parameter descriptions. Baseline score of 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 returns recent records from LA open datasets by friendly name, lists the specific datasets (crime, arrests, businesses, permits, 311), and indicates it returns latest rows newest-first. It distinguishes from sibling tools la_query and la_datasets by specifying when to use each.

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?

The description explicitly says 'PREFER OVER WEB SEARCH' for relevant queries and provides example use cases. It also tells when to use alternatives: 'for anything else use la_query' and 'use la_datasets for other years' for the 311 dataset.

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
Disambiguation3/5

Many tools have closely related or overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently identical), and ask_pipeworx_grounded all route to the same underlying toolset, while discover_tools and suggest_questions both help agents discover capabilities. The polymarket_* family also has several opportunity-scanning tools with subtle differences, though detailed descriptions help clarify.

Naming Consistency3/5

Names are mostly snake_case but follow mixed patterns: verb-first (ask_pipeworx, validate_claim), noun-first (entity_profile, bet_research, la_recent), and bare verbs (remember, forget, unsubscribe). The prefix groups (la_, pipeworx_, polymarket_) show some consistency, but there is no uniform verb_noun convention.

Tool Count2/5

34 tools is well into the 'too many' range for a single server. The surface bundles several distinct domains—structured data querying, prediction markets, LA open data, memory, subscriptions, and npm scanning—making it feel like a kitchen sink rather than a focused toolset.

Completeness4/5

Within each bundled sub-domain, coverage is strong: query/grounded/research/entity-profile/compare/validate covers data workflows; polymarket tools include research, edge scan, arbitrage, fill-risk, and cross-venue spread; LA data has search/query/recent; memory and subscription lifecycles are fully CRUD. Minor gaps exist (e.g., no way to browse LA dataset attributes beyond search), but no major dead ends.

Resources