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Horizons Nasa

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 indicate read-only, idempotent, non-destructive. Description adds details on GDELT→GNews fallback, USPTO soft-fail, and return structure (changes, total_changes, citation URIs).

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?

Front-loaded with example queries, every sentence adds value. Slightly long but still efficient for the information density.

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?

No output schema, but description explains return format and sources. Covers date formats, fallback behavior, and limitations (USPTO sunset). Fully sufficient for a read-only aggregation tool.

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 description adds practical guidance: since accepts ISO or relative, recommends '30d'/'1m', value explains ticker/CIK. Enriches beyond 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?

Clearly states it returns a change feed for a company from multiple sources (SEC, GDELT/GNews, USPTO) and explicitly distinguishes from sibling tool entity_profile.

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?

Provides explicit usage examples ('What's new with X') and directs when to use entity_profile instead. No ambiguity.

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
Disambiguation5/5

Each tool has a clear, distinct purpose with detailed descriptions that differentiate overlapping capabilities (e.g., ask_pipeworx vs deep_research vs ask_pipeworx_grounded vs bet_research). No two tools appear redundant; even similar prediction-market tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) have specific scopes.

Naming Consistency5/5

Tool names consistently use lowercase snake_case (e.g., ai_visibility_check, compare_entities, pipeworx_trending, polymarket_kalshi_spread). Single-word exceptions (ephemeris, lookup, observers, recall, remember, vectors) are common short verbs and do not break the pattern. No mixing of camelCase or other conventions.

Tool Count4/5

35 tools is above the typical 3-15 range, but the server is a comprehensive data platform covering multiple domains (SEC, FDA, FRED, prediction markets, memory, subscriptions, feedback). The count is justified given the breadth; it feels slightly heavy but not bloated or redundant.

Completeness5/5

The tool surface covers the full lifecycle for a data/research platform: discovery (discover_tools, suggest_questions), entity resolution (resolve_entity), lookups (ask_pipeworx, entity_profile), comparison (compare_entities), validation (validate_claim), prediction-market operations (polymarket_*), memory (remember/recall/forget), subscriptions (subscribe/unsubscribe/list_subscriptions/recent_alerts), and meta/feedback (pipeworx_feedback, pipeworx_trending). No obvious gaps for typical workflows.

Resources