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Glama

Oregon Open Data

by io.github.pipeworx-io

Query

query
Read-onlyIdempotent

Run a Socrata SoQL query against a Oregon Open Data dataset by resource_id (e.g. "tckn-sxa6"). 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. "tckn-sxa6" (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": 50,
+    "resource_id": "tckn-sxa6",
+    "where": "year >= 2020"
+  },
+  {
+    "group": "category",
+    "order": "total DESC",
+    "resource_id": "tckn-sxa6",
+    "select": "category, count(*) AS total"
+  }
+]
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already provide readOnlyHint, destructiveHint, idempotentHint, and openWorldHint, covering safety traits. The description adds useful nuance: returns matching rows as JSON and SoQL clauses should omit the leading '$'. This goes beyond the schema but does not detail error handling, rate limits, or pagination behavior comprehensively.

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 three sentences, with no fluff or repetition. The first sentence clearly states the purpose, the second lists clauses, and the third covers the return format. It is slightly more verbose than the tightest descriptions but is well-structured and 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?

For a query tool with 7 parameters and no output schema, the description covers the essential return type (JSON), the query syntax, and the target resource. The schema examples fill in usage patterns. Missing details like defaults or error handling are minor for this context, and annotations cover safety.

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 coverage is 100%, with each parameter having a description and examples. The description reiterates the main clause types and adds the '$' omission rule, which is a small extra. It does not significantly compensate beyond the schema, so baseline 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 runs a Socrata SoQL query against an Oregon Open Data dataset by resource_id, naming the resource and specific operation. It distinguishes itself from siblings like 'datasets' (which lists datasets) and 'metadata' by focusing on data retrieval with filtering capabilities.

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 context is explicit: use this tool when you need to query a specific dataset using SoQL clauses. However, it does not provide explicit alternatives or when-not-to-use scenarios, such as mentioning 'datasets' for discovering resource_ids or 'search_within' for simpler searches.

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

B3.4/5.0
Disambiguation1/5

The tool set is a chaotic mix of unrelated domains: Oregon Open Data tools (datasets, metadata, query) are buried among dozens of tools for Pipeworx general query, Polymarket betting, memory management, and AI visibility. Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim) making it impossible for an agent to distinguish the right tool for a given task without deep inspection.

Naming Consistency1/5

Naming is wildly inconsistent: snake_case (ai_visibility_check, pipeworx_feedback), camelCase (bet_research, datasets, metadata, query), mixed (ask_pipeworx_grounded, polymarket_arbitrage). No consistent verb_noun or pattern exists, and many names are vague (remember, recall, forget) without connection to the server's assumed domain.

Tool Count2/5

33 tools is excessive for a server ostensibly about Oregon Open Data, which only has 3 relevant tools. The remaining 30 are from other services (Pipeworx, Polymarket, etc.) and do not belong, making the count inappropriate for the server's declared purpose.

Completeness2/5

For the Oregon Open Data domain, the surface is bare: only search, metadata, and query. Missing operations like upload, update, or delete datasets. The heavy presence of unrelated tools (betting, memory, AI visibility) does not compensate for the gap in the actual domain coverage.

Resources