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Data Norfolk

by io.github.pipeworx-io
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Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Norfolk Open Data dataset by resource_id (e.g. "cab7-wvn5"). 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. "cab7-wvn5" (from datasets).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • changedInput schema / examples
      Previous value: -[
-  {
-    "limit": 50,
-    "resource_id": "cab7-wvn5",
-    "where": "year >= 2020"
-  }
-]New value: +[
+  {
+    "limit": 20,
+    "resource_id": "cab7-wvn5"
+  }
+]
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 50,
+    "resource_id": "cab7-wvn5",
+    "where": "year >= 2020"
+  }
+]
  3. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already establish read-only, idempotent, non-destructive behavior. The description adds meaningful behavioral context beyond that: it clarifies SoQL clause syntax ('without the leading $'), which is essential to avoid errors, and states that the operation returns matching rows as JSON. This exceeds what the annotations alone convey.

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 cover the tool's purpose, the key resource identifier, the available SoQL clauses, the important '$' syntax caveat, and the return type. The information is front-loaded and every sentence contributes value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a read-only query tool with full parameter documentation in the schema, this description is complete: it names the endpoint type, the identifier format, the filtering options, pagination controls, and the JSON return format. No critical behavior needed for correct invocation is missing.

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 description coverage is 100%, so every parameter is already documented with names, types, and examples. The description reiterates clause semantics at a high level but does not add significant meaning beyond the schema's per-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 names a specific verb ('Run'), a specific resource type ('Socrata SoQL query against a Norfolk Open Data dataset'), and the required identifier (resource_id), making the tool's function unmistakable. It also lists the main filter clauses and the JSON return format, which distinguishes it from sibling tools like datasets or metadata.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description clearly explains how to use the tool and what clauses are available, and the schema implies the resource_id comes from datasets. However, it never explicitly contrasts this tool with alternatives or states when not to use it, leaving the agent to infer selection based on the resource_id context.

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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