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Glama

Connecticut 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").

TDQS

A4.9/5.0
Behavior5/5

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

Beyond annotations (readOnlyHint, etc.), the description discloses fan-out to multiple sources, fallback logic (GDELT→GNews), soft-fail for USPTO, and return structure (grouped by source with citation URIs). No contradiction 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is comprehensive but slightly lengthy; however, every sentence adds necessary detail and it is well front-loaded with examples. Minor redundancy in the parameter descriptions.

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?

Given the tool's complexity (multiple sources, fallback, parameter options) and absence of output schema, the description thoroughly explains behavior, return structure, and includes an alternative tool. No gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description adds significant value: explains 'since' accepts ISO or relative shorthand with examples, clarifies 'type' is limited to 'company', and notes 'value' can be ticker or CIK.

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 provides a change feed for a company over a time window, with example queries. It explicitly distinguishes from the sibling tool 'entity_profile' which serves static profiles.

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

Usage Guidelines5/5

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

The description provides explicit guidance on when to use this tool (change feed) vs. 'entity_profile' (static profile), recommends typical 'since' values like '30d' or '1m', and explains fallback behavior for news sources.

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

A3.5/5.0
Disambiguation1/5

Multiple tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) have overlapping research purposes. Additionally, many Polymarket and Pipeworx-specific tools are unrelated to the Connecticut Open Data server name, causing confusion.

Naming Consistency2/5

Naming is highly inconsistent: some use snake_case (ask_pipeworx, resolve_entity), others use longer descriptive phrases (polymarket_fill_risk, scan_competitor_ai_presence), with no clear pattern.

Tool Count1/5

The server claims to be about Connecticut Open Data but includes 34 tools, only 3 of which (datasets, metadata, query) are relevant. The vast majority are unrelated Pipeworx/Prediction Market tools, making the size inappropriate.

Completeness1/5

For Connecticut Open Data coverage, only basic dataset search, metadata, and query tools exist. Missing common operations like data upload, schema modification, or API key management for the open data portal.

Resources