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Attom

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?

Annotations declare readOnlyHint, openWorldHint, idempotentHint true and destructiveHint false. The description adds significant behavioral context: fans out to multiple sources in parallel, fallback from GDELT to GNews, soft-fail for USPTO, and describes output format (changes grouped by source, total_changes, pipeworx:// URIs). No contradictions noted.

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 structured with front-loaded example queries, then details on sources, parameters, and output. Every sentence adds value; there is no redundancy or filler. It efficiently conveys all necessary information.

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 complexity (multiple data sources, fallback logic, output format) and lack of output schema, the description thoroughly covers behavior and return structure. It also correctly contextualizes the tool among siblings by pointing to entity_profile for static needs.

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%. The description elaborates on each parameter: 'type' explains it's only 'company', 'since' gives ISO date and relative shorthand examples ('7d', '30d', '3m', '1y') with recommendation ('Use 30d or 1m for typical monitoring'), and 'value' explains ticker or zero-padded CIK. 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 explicitly states the tool's purpose: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It gives example queries like 'latest on Y' and lists sources (SEC EDGAR, GDELT/GNews, USPTO), clearly distinguishing from sibling 'entity_profile' by directing users to use that 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 usage guidance with natural language examples ('What's new with X', 'updates on Acme') and specifies when not to use it: 'Use entity_profile instead when you want the static profile...' This clearly differentiates between the two tools.

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
Disambiguation2/5

Several tools overlap heavily: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and ask_pipeworx_grounded shares the same router. The universal ask_pipeworx router also subsumes many domain-specific tools (attom_*, entity_profile, etc.), making it unclear when to use the specialist tools versus the catch-all.

Naming Consistency3/5

All names are snake_case and mostly descriptive, but conventions vary: ask_* and attom_* prefixes coexist with bare verbs (remember, forget, subscribe), noun phrases (entity_profile, polymarket_edges), and adjective-prefixed names (recent_alerts, recent_changes). The pattern is readable but not uniform.

Tool Count2/5

39 tools is well over the 25+ threshold for a heavy surface, especially for a server named 'Attom' that also includes memory, subscriptions, feedback, npm scanning, and AI-visibility tools beyond real estate. Many tools could be consolidated (e.g., the three ask_pipeworx variants, the six polymarket tools).

Completeness4/5

The real estate domain is well covered (search, detail, AVM, rental AVM, sales history, trends, assessment, schools), and the broader data platform includes discovery, grounded answers, entity profiles, comparisons, claim validation, subscriptions, and memory. Minor gaps exist only around edge features like OAuth-gated subscription persistence.

Resources