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Dart Kr

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

A5/5.0
Behavior5/5

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

Beyond annotations (readOnly, idempotent, etc.), the description details the fan-out to multiple sources, the GDELT→GNews fallback, USPTO soft-fail, and the return structure. This adds significant behavioral context without contradiction.

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 a single efficient paragraph that front-loads the purpose with example queries, then covers sources, parameters, return format, and the sibling tool note. Every sentence contributes distinct information, with no wasted words.

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 covers the return structure (changes, total_changes, citation URIs), source behavior, parameter semantics, and provides a usage guideline. The tool's complexity is fully addressed.

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 value by explaining the `since` parameter's formats (ISO date, relative shorthand) with examples, clarifying `type` is limited to 'company', and specifying `value` can be ticker or CIK. This goes beyond the schema.

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 returns a change feed for a company in a time window, with specific verb+resource from example queries. It distinguishes from the sibling entity_profile, making the purpose unambiguous.

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?

Explicitly says when to use (change feed) and when not to (static profile), naming the alternative tool (entity_profile). Provides example queries that help the agent identify usage scenarios.

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_beta deliberately matches ask_pipeworx exactly right now, discover_tools and suggest_questions both serve as what-can-I-do entry points, and ai_visibility_check is just the single-entity version of scan_competitor_ai_presence. An agent will struggle to pick the right variant without carefully reading long descriptions.

Naming Consistency3/5

All tools are snake_case and several families share clear prefixes (dart_*, polymarket_*, ask_pipeworx_*), but the overall set mixes verb_noun (discover_tools, validate_claim), noun_phrase (entity_profile, deep_research), bare verbs (remember, recall, forget), and prefix-noun (dart_financials). Readable but not unified.

Tool Count2/5

36 tools is far above the 25+ heavy threshold, and the count is inflated by redundancy: ask_pipeworx_beta is a literal duplicate today, suggest_questions overlaps discover_tools, and ai_visibility_check is subsumed by scan_competitor_ai_presence. The broad Pipeworx platform justifies many tools, but the exposed surface is bloated.

Completeness4/5

The surface covers the apparent domain well: universal querying (ask_pipeworx family + deep_research), tool discovery, entity resolution, profiles, comparisons, change feeds, claim verification, Korean DART filings, Polymarket analysis/fill-risk, memory, subscriptions, and feedback. Minor gaps remain—there's no explicit fetch-by-citation-URI tool despite claims those URIs are fetchable, and no way to retrieve full DART filing text beyond discovery.

Resources