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Glama

Utah Open Data

by io.github.pipeworx-io

Recent Changes

recent_changes
Read-onlyIdempotent

"What's new with X" / "latest on Y" / "what happened to Z this week / month / quarter" / "updates on Acme" / "news on Tesla recently" / "what's happening with Apple" — change feed for a company in the last N days/weeks/months in ONE parallel call. Fans out to SEC EDGAR (filings since since), GDELT→GNews fallback (news mentions in window — GDELT preferred, GNews when rate-limited or 5xx), USPTO (patents granted; PatentsView API sunset May 2025 so this soft-fails until reactivated). since accepts ISO date ("2026-04-01") or relative shorthand ("7d", "30d", "3m", "1y"). Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs. Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type. Only "company" supported today.
sinceYesWindow start — ISO date ("2026-04-01") or relative ("7d", "30d", "3m", "1y"). Use "30d" or "1m" for typical monitoring.
valueYesTicker (e.g., "AAPL") or zero-padded CIK (e.g., "0000320193").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already indicate safe, read-only, idempotent behavior. The description adds transparency about the fan-out to multiple sources (SEC EDGAR, GDELT→GNews, USPTO) and the fallback logic. It also describes the return format (structured changes, total_changes, citation URIs), adding value beyond annotations.

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 a single dense paragraph that conveys a lot of information without waste. It is front-loaded with example queries. While it could benefit from slight structural separation (e.g., listing sources), it remains concise and readable.

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?

Despite no output schema, the description fully explains the return format (grouped changes, total_changes count, citation URIs). It covers all behavioral aspects: multiple sources, fallback, accepted inputs, and even a note about USPTO soft-fail. It is complete given the tool's complexity.

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 coverage is 100%, so baseline is 3. The description enriches parameters by clarifying that `value` can be a ticker or CIK, and suggests '30d' or '1m' for typical monitoring. It also explains the `since` shortcuts (7d, 30d, 3m, 1y) and ISO format, which goes beyond the schema 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 opens with concrete example queries ('What's new with X' / 'latest on Y') and explicitly states it returns a change feed for a company in a time window. It clearly distinguishes from entity_profile, making the purpose unmistakable.

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 guidance on when to use this tool (e.g., 'Use entity_profile instead when you want the static profile') and explains fallback behavior (GDELT→GNews). It also explains accepted `since` formats. However, it could be more explicit about when not to use it.

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

A3.8/5.0
Disambiguation2/5

The toolset contains several near-duplicate clusters: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all route the same kinds of questions, and the six polymarket tools have heavily overlapping scopes. An agent would frequently struggle to pick the right variant despite the detailed descriptions.

Naming Consistency4/5

Names consistently use lowercase snake_case with a verb-first or domain-prefixed pattern (ask_pipeworx, resolve_entity, validate_claim, polymarket_arbitrage). Minor deviations like bare nouns (datasets, metadata) are acceptable but not perfectly uniform.

Tool Count2/5

34 tools is far beyond what a Utah Open Data server needs; only 3 tools actually relate to the named domain. The rest form a sprawling general-purpose Pipeworx/prediction-market platform, making the surface feel bloated for its stated purpose.

Completeness3/5

For the actual Utah Open Data catalog, datasets/query/metadata is a complete read-only surface. But for the broader Pipeworx functionality the set actually delivers, there are odd gaps (no account management beyond subscriptions) and many irrelevant tools, so overall coverage is uneven.

Resources