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Data Cookcounty

by io.github.pipeworx-io
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Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Cook County Open Data dataset by resource_id (e.g. "cjeq-bs86"). Filter with where/select/group/order (SoQL clauses, without the leading $) plus limit/offset. Returns matching rows as JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
groupNoSoQL $group column(s).
limitNoMax rows (default Socrata 1000).
orderNoSoQL $order, e.g. "date DESC".
whereNoSoQL $where filter, e.g. "year >= 2020 AND status = 'Active'".
offsetNoPagination offset.
selectNoSoQL $select, e.g. "name, count(*) AS n".
resource_idYesDataset id, e.g. "cjeq-bs86" (from datasets).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "limit": 100,
-    "resource_id": "cjeq-bs86",
-    "where": "year >= 2020"
-  },
-  {
-    "group": "category",
-    "order": "count DESC",
-    "resource_id": "cjeq-bs86",
-    "select": "category, count(*) AS count"
-  }
-]New value: +[
+  {
+    "limit": 20,
+    "resource_id": "cjeq-bs86"
+  },
+  {
+    "limit": 20,
+    "resource_id": "cjeq-bs86",
+    "select": "casenumber"
+  }
+]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 100,
+    "resource_id": "cjeq-bs86",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "category",
+    "order": "count DESC",
+    "resource_id": "cjeq-bs86",
+    "select": "category, count(*) AS count"
+  }
+]
  3. 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, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds valuable behavioral details beyond annotations: it specifies the return format (JSON) and clarifies that SoQL clauses should be provided without the leading $, which is a common usage trap. No contradiction 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?

Two sentences with no fluff. The first sentence states the main action and example, the second lists filtering options and return format. Every word earns its place, and the key information is front-loaded.

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?

The description covers the return format (JSON rows), the required resource_id, and the filtering/pagination options. Since there is no output schema, it explains what the tool returns. It doesn't mention error handling or rate limits, but for a read-only tool with these annotations, the information is sufficient for an agent to call it 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 description coverage is 100%, so each parameter is already well-documented. The description adds a useful clarification about the $ prefix in SoQL clauses, which is not in the schema, and lists the available clauses (where/select/group/order). This adds meaningful value beyond the schema's parameter 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 clearly states the tool runs a Socrata SoQL query against a Cook County Open Data dataset, giving a specific resource_id example. It distinguishes from sibling tools like 'datasets' (which lists datasets) and 'metadata' (which retrieves metadata) by focusing on querying rows.

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 provides clear context on when to use this tool: when you need to query rows with filters (where/select/group/order) and pagination. However, it does not explicitly name alternatives or exclusions, such as using 'datasets' to find resource_ids or 'metadata' for dataset info. The usage is implied but not explicitly routed.

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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