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newton

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").

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Beyond the readOnly/idempotent/destructive annotations, the description discloses multi-source fan-out, rate-limit fallbacks, a known API sunset (PatentsView May 2025), and the return shape (structured changes[] + total_changes + citation URIs). This is substantial behavioral context not inferable from annotations alone.

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?

Even though long, every sentence contributes essential information: usage examples, source fallbacks, limitations, parameter hints, return structure, and alternative tool. The dense but well-organized single paragraph is front-loaded with query examples and wastes no 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?

For a tool with multiple upstream APIs, rate-limit fallbacks, and a planned sunset, the description covers all key aspects: what changes are included, how the window is specified, what to expect back, and when to use a sibling tool. No output schema exists, so the return description is essential and provided.

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 coverage is 100%, so baseline is 3. The description adds practical value by suggesting '30d' or '1m' for monitoring, reinforcing the ticker/CIK formats, and clarifying the `since` window semantics with examples. It goes slightly beyond a simple schema restatement.

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 opens with realistic query examples and clearly states the tool is a 'change feed for a company in the last N days' citing specific sources (SEC EDGAR, GDELT/GNews, USPTO). It explicitly distinguishes itself from the sibling entity_profile, making its scope unmistakable.

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 concrete usage guidance with phrase examples, explains the `since` parameter with typical values, and explicitly names an alternative tool ('Use entity_profile instead when you want the static profile'). It also notes fallback behavior (GDELT → GNews) and the USPTO soft-fail condition.

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

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple ways to get entity information (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities) and numerous prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Despite detailed descriptions, the boundaries are unclear, making it difficult for an agent to distinguish between them.

Naming Consistency4/5

Tool names mostly follow a snake_case convention and are generally descriptive. Minor inconsistencies exist, such as 'discover_tools' vs. 'scan_competitor_ai_presence' and a few single-word verbs like 'derive' and 'remember'. Overall, the pattern is predictable and readable.

Tool Count2/5

With 34 tools, the server is heavy for a single server. The scope is very broad, covering math, memory, data retrieval, prediction markets, and more. While each tool has a specific purpose, the high count suggests a lack of focus and could overwhelm an agent.

Completeness4/5

The server offers extensive coverage for data retrieval, entity lookup, comparison, research, and prediction markets. Minor gaps exist, such as missing advanced math operations (e.g., solving equations) and some niche data sources, but the core workflows are well-covered with tools like ask_pipeworx and deep_research.

Resources