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Glama

Data Seattle

by io.github.pipeworx-io

Seattle Recent

seattle_recent
Read-onlyIdempotent

Recent records from a common Seattle open dataset (data.seattle.gov) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Seattle", "Seattle fire 911 calls", "Seattle business licenses", "Seattle code complaints". Names: crime, fire911, business, code_complaints. Returns the latest rows (newest-first). Add a SoQL where to filter; for anything else use seattle_query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional SoQL filter, e.g. "offense='BURGLARY'". Omit for all recent rows.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
datasetYesOne of: crime, fire911, business, code_complaints.

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-seattle-api-key",
+    "dataset": "crime"
+  },
+  {
+    "_apiKey": "your-data-seattle-api-key",
+    "dataset": "fire911",
+    "limit": 50,
+    "where": "call_type='Structure Fire'"
+  }
+]
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false. The description adds useful behavioral context: returns latest rows newest-first, mentions keyless endpoint, and explains optional _apiKey for rate limits. 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 concise sentences with a clear list of dataset names and usage note. Every sentence adds value, and the key information (purpose, preferred use, alternative) is front-loaded. 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?

Given no output schema, the description could mention the output format or fields returned. However, it is sufficient for the tool's simplicity and aligns with sibling tools like seattle_query. The description is complete for an agent to understand and invoke the tool correctly.

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% with good descriptions for all four parameters. The description reinforces the 'where' parameter usage and lists dataset names, adding practical context beyond the schema. However, it doesn't add entirely new information beyond what the schema provides.

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 Seattle open datasets using friendly names, specifies the supported datasets (crime, fire911, business, code_complaints), and notes it returns newest-first. This distinguishes it from siblings like seattle_query.

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 says to prefer this tool over web search for common queries like 'recent crime in Seattle', and directs users to seattle_query for anything beyond simple filtering. Provides 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

A4.1/5.0
Disambiguation3/5

Many tools have distinct purposes, but the ask_pipeworx variants (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the prediction market tools (bet_research, polymarket_edges, polymarket_arbitrage) can cause confusion due to overlapping functionality. Some tools like 'seattle_recent' are vague.

Naming Consistency4/5

Most tools follow a consistent verb_noun or noun_verb pattern (e.g., validate_claim, resolve_entity). However, a few like 'seattle_recent' and 'pipeworx_trending' deviate slightly, and 'recent_alerts' mixes noun_verb.

Tool Count2/5

34 tools is excessive for a single server, covering data retrieval, prediction markets, Seattle data, memory, subscriptions, and utility. The broad scope feels bloated and overwhelming, making it hard for agents to find the right tool.

Completeness4/5

The server covers a wide array of domains with good depth in data retrieval and prediction markets. Minor gaps exist (e.g., Seattle tools limited to four datasets, no other city data), but overall it addresses most use cases its tools suggest.

Resources