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Glama

Data Santaclara

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Santa Clara County Open Data dataset by resource_id (e.g. "n9u6-aijz"). 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. "n9u6-aijz" (from datasets).

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: +[
+  {
+    "limit": 100,
+    "resource_id": "n9u6-aijz",
+    "where": "year >= 2020"
+  }
+]
  2. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false, which the description does not contradict. The description adds behavioral context: the syntax (SoQL clauses without leading $) and the return format (JSON). This is sufficient for a read-only query tool.

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?

The description is two sentences long with no filler. Each sentence serves a purpose: one states the action and inputs, the other specifies the output format. It is front-loaded with the most critical information.

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 7-parameter tool with full schema coverage, the description provides adequate context. It specifies the output format (JSON) and hints at syntax conventions. Though it lacks details on error handling or pagination, annotations and schema cover critical behavior. The absence of an output schema is compensated by the description stating 'Returns matching rows as JSON.'

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%, providing baseline clarity. The description adds the nuance that SoQL clauses should not include the leading '$$', and includes an example in the schema examples. This aids interpretation but does not go beyond what the schema already explains.

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 on a Santa Clara County Open Data dataset, specifying the resource_id and available clauses (where/select/group/order, limit/offset). This distinctively separates it from sibling tools, none of which perform SoQL queries.

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 implicitly guides usage by specifying the exact context: Socrata SoQL against a specific dataset ID. However, it does not explicitly mention when to avoid using this tool or compare it with alternatives like 'datasets' for discovering available datasets.

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

A3.9/5.0
Disambiguation2/5

Multiple tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and validate_claim all route questions to the same 5,798-tool catalog and can return similar evidence-backed answers. Additionally, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_edge_tracker, and bet_research all circle prediction-market edge detection, creating boundary ambiguity despite detailed descriptions.

Naming Consistency3/5

Most tools follow a verb_noun or noun_verb pattern (e.g., list_subscriptions, generate_llms_txt, scan_dependency, compare_entities, resolve_entity), and consistent snake_case is used throughout. However, some names are vague and unclear (query, metadata, recall, forget, datasets), and the ask_pipeworx family is not clearly versioned in naming.

Tool Count2/5

34 tools is heavy for a server that is conceptually a data-access gateway plus a few meta utilities. The count is inflated by multiple near-duplicate research modes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) and an extensive prediction-market subfamily that could be consolidated.

Completeness4/5

The server covers its main domains well: entity resolution, company profiles, comparisons, change feeds, claim verification, grounded Q&A, and data discovery all exist. Minor gaps include no obvious tool for general web search or full-text legal records, and the subscription/alert system lacks an update-subscription tool, but core workflows have no dead ends.

Resources