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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 indicate read-only, open-world, idempotent, non-destructive behavior. The description adds specific behavioral details: parallel fan-out across three sources, GDELT→GNews fallback, soft-fail for USPTO, and structured return format with citation URIs. No contradictions.

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 front-loads the core purpose with example queries, then efficiently covers multiple data sources, fallback mechanisms, return structure, and comparison to sibling—all in a dense but readable paragraph. Every sentence adds value.

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 states the return format (changes[] grouped by source, total_changes count, citation URIs) and covers all relevant behavioral aspects. No gaps for an agent to make informed decisions.

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 covers all three parameters (100% coverage), so baseline is 3. The description adds helpful usage advice for the 'since' parameter (e.g., "Use '30d' or '1m' for typical monitoring") and clarifies that 'type' only supports 'company'—adding value beyond the schema alone.

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 uses specific verbs ("change feed for a company") and lists concrete sources (SEC EDGAR, GDELT/GNews, USPTO). It clearly distinguishes from the sibling tool entity_profile, which is explicitly mentioned as the alternative for 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 query examples, explains when to use this tool versus entity_profile, and includes operational details like fallback behavior (GDELT preferred, GNews when rate-limited) and date shorthand options.

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

A4/5.0
Disambiguation3/5

The tool set includes several overlapping research/lookup tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile) and multiple Polymarket tools, which could confuse an agent despite detailed descriptions. The boundaries between these tools are explained in the descriptions, but the sheer number of similar-purpose tools creates ambiguity.

Naming Consistency4/5

Most tools follow a verb_noun pattern (e.g., search_crates, get_versions, validate_claim), but there are exceptions like entity_profile, deep_research, and bet_research, which break the pattern. The overall naming is readable and mostly consistent, with the polymarket_ and pipeworx_ prefixes providing grouping.

Tool Count2/5

With 35 tools, the server is far too heavy for a focused service. The server name 'Crates' suggests a narrow domain, but only 5 tools relate to Rust crates, while the rest cover disparate areas (Pipeworx, Polymarket, memory management). This mismatch and high count make the tool surface unwieldy.

Completeness4/5

For the underlying Pipeworx/Polymarket domain that the majority of tools serve, the coverage is strong: lookup, grounded answers, research, entity profiles, comparisons, changes, claim validation, scanning, memory, subscriptions, tool discovery. Minor gaps exist (e.g., no direct summarization), but the set feels well-rounded for a data analysis agent.

Resources