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Trove

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?

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description adds significant behavioral context: fan-out to multiple data sources (SEC, GDELT, GNews, USPTO), fallback logic, and the patent API sunset in May 2025 causing soft-fails. This goes well beyond what annotations provide.

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 relatively long (three sentences) but every sentence adds value: first sentence provides usage examples, second details data sources and fallbacks, third explains return format and alternative tool. It is front-loaded with example queries, making it efficient despite length.

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 lacking an output schema, the description clearly explains the return structure: changes[] grouped by source, total_changes count, and pipeworx:// citation URIs. This covers all necessary context for a complex multi-source tool, making it complete for agent decision-making.

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 enriches parameter semantics significantly: it explains that 'since' accepts ISO dates or relative shorthand (with examples like '30d'), clarifies that 'value' can be a ticker or zero-padded CIK, and notes that 'type' currently only supports 'company'. This adds meaning 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 provides a change feed for a company, returning filings, news, and patents. It explicitly distinguishes itself from the sibling tool 'entity_profile' which returns static profiles, ensuring the agent knows when to use this tool.

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 use cases ('What's new with X', 'latest on Y') and directs the agent to use 'entity_profile' for static profile requirements. It also details fallback mechanisms (GDELT→GNews) and the soft-fail for patents, giving clear guidance on tool behavior.

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

A3.7/5.0
Disambiguation3/5

Several tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research all answer questions; entity_profile, compare_entities, recent_changes all cover company data). Descriptions provide distinctions, but an agent can easily misselect, especially between the Pipeworx query tools.

Naming Consistency2/5

Tool names follow mixed conventions: some use verb_noun (list_subscriptions, unsubscribe), others use descriptive phrases (ai_visibility_check, polymarket_arbitrage) or nouns (deep_research, entity_profile). No consistent pattern, making it harder to predict tool names.

Tool Count2/5

With 32 tools covering diverse domains (data lookups, prediction markets, memory, subscriptions), the server feels overloaded. The scope would be better served by splitting into smaller, focused servers (e.g., data query, prediction market, memory). Many tools are peripheral to a core purpose.

Completeness4/5

The tool surface is fairly comprehensive within its domains: CRUD for memory (remember/recall/forget), subscription management, extensive data query options, and prediction market analysis. Minor gaps exist (no update memory, no direct trading), but agents can work around them.

Resources