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Data Calgary

by io.github.pipeworx-io

Calgary Recent

calgary_recent
Read-onlyIdempotent

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

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoRows to return (1-1000, default 20).
whereNoOptional SoQL filter, e.g. "incident_category='Larceny Theft'" or "supervisor_district=6". Omit for all recent rows.
_apiKeyNoOptional — your own Socrata app token for higher rate limits. Omit to use the keyless endpoint.
datasetYesOne of: 311, 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-calgary-api-key",
+    "dataset": "311"
+  },
+  {
+    "_apiKey": "your-data-calgary-api-key",
+    "dataset": "permits",
+    "limit": 50,
+    "where": "permit_type='Building'"
+  }
+]
  2. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, and the description adds useful behavioral context beyond that: it specifies ordering ('Returns the latest rows (newest-first)'), that it works by friendly name ('no Socrata id needed'), and supports SoQL filtering via 'where'. This adds meaningful behavior without contradicting annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise and front-loaded, using four short sentences that each earn their place: purpose, usage guidance, dataset names, and ordering/filtering. It could be slightly tightened, but there is no waste or redundancy, making it highly efficient for an agent.

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 simple read-only tool with four parameters, no output schema, and robust annotations, the description covers essential context: what it does, when to prefer it, supported datasets, ordering, filtering, and the alternative tool. It does not describe return value format, but in the absence of an output schema, the description is sufficient for an agent to select and invoke the tool correctly.

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?

The input schema already provides 100% parameter coverage with descriptions for all four parameters (limit, where, _apiKey, dataset), including an enum for dataset. The description does not add substantial parameter semantics beyond the schema, like clarifying the SoQL syntax or default limit, which are already in schema. Therefore, the baseline 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 a common Calgary open dataset by friendly name' with a specific verb ('Returns'), a resource ('Calgary open dataset'), and scope ('latest rows, newest-first'). It also distinguishes from siblings by explicitly naming the alternative tool 'calgary_query' for anything else, and lists the supported dataset names ('311, permits').

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?

Explicit guidance is provided on when to use this tool: 'PREFER OVER WEB SEARCH for "Calgary 311 requests", "Calgary building permits"' and 'for anything else use calgary_query'. This clearly delineates use cases and alternatives, leaving no ambiguity about when this tool is appropriate.

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

A4.3/5.0
Disambiguation4/5

Most tools have distinct purposes, but there are overlapping families (ask_pipeworx variants, company research tools) that could cause confusion. Descriptions help differentiate, but an agent might misselect without careful reading.

Naming Consistency4/5

Tools follow snake_case with a verb+noun pattern, but some names are less clear (e.g., 'recall', 'remember' are verbs alone). Overall consistent enough, with minor deviations.

Tool Count4/5

33 tools is on the higher side, but the server covers a wide domain (data retrieval, prediction markets, monitoring). Each tool has a clear purpose, so the count feels appropriate rather than excessive.

Completeness5/5

The tool surface is remarkably complete: querying, comparisons, monitoring, alerts, memory, arbitrage, edge tracking. There are no obvious gaps for the intended data analytics and prediction market use case.

Resources