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Data Howardcounty

by io.github.pipeworx-io
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Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Howard County Open Data dataset by resource_id (e.g. "qccx-65fg"). 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. "qccx-65fg" (from datasets).

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "limit": 100,
+    "resource_id": "qccx-65fg",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "category",
+    "order": "count DESC",
+    "resource_id": "qccx-65fg",
+    "select": "category, count(*) AS count"
+  }
+]
  2. First observed

TDQS

A3.9/5.0
Behavior4/5

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

The description discloses key behaviors: it queries a specific dataset, uses SoQL syntax without leading $, returns JSON rows, and mentions a default limit (Socrata 1000). These details add value beyond the annotations (readOnly, idempotent, non-destructive). 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?

Two well-structured sentences: the first states the action and data source, the second lists the parameters. Information is front-loaded with the most critical details. Every sentence is meaningful and concise, with 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?

The description covers the core functionality: querying a dataset with SoQL, returning JSON, and listing parameters. It mentions default limits but does not address error handling, pagination beyond offset, or output schema. Given the tool's complexity and the absence of an output schema, the description is sufficiently complete for most use cases.

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?

With 100% schema description coverage, the schema already documents each parameter thoroughly. The description adds value by clarifying SoQL syntax (no leading $) and providing examples. This improves understanding beyond the schema alone.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's action ('Run a Socrata SoQL query') and the target ('Howard County Open Data dataset by resource_id'). It provides a concrete example of a resource_id, making the purpose unambiguous. However, it does not explicitly differentiate from sibling tools like 'datasets' or 'search_within'.

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 lists the SoQL clauses and pagination parameters, giving clear usage context. It also provides examples of valid parameter combinations. However, it omits guidance on when not to use this tool, such as when searching for datasets or performing simpler lookups, which could lead to suboptimal tool choice.

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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