Skip to main content
Glama

Oxylabs

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. First observed

TDQS

A4.8/5.0
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. Description adds behavioral details: fans out to multiple sources, GDELT preferred over GNews, PatentsView soft-fail until reactivated, and returns structured changes with URIs. No contradiction with annotations.

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?

Description is front-loaded with example queries and well-organized, though somewhat lengthy with multiple parenthetical notes. Every sentence adds value, but could be slightly trimmed without losing clarity.

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 exists, but description specifies return structure (changes[] grouped by source, total_changes count, citation URIs). Covers edge cases (GDELT→GNews fallback, PatentsView soft-fail) and provides all necessary context for correct usage.

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 describes all parameters (100% coverage). Description adds value by explaining type is restricted to 'company', since accepts ISO date or relative shorthand with examples, and value accepts ticker or CIK. Adds practical guidance like using '30d' for typical monitoring.

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, listing specific sources (SEC EDGAR, GDELT/GNews, USPTO) and example queries. It distinguishes from sibling tool entity_profile by contrasting static vs. dynamic data.

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?

Explicitly gives example queries and specifies when to use entity_profile instead. Provides context for typical windows (e.g., '30d' for monitoring) and clarifies data sources and their priority.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Oxylabs toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation3/5

Several tools overlap in purpose, particularly the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) where beta currently matches stable exactly, and the polymarket_* cluster with similar names. However, detailed descriptions clarify each tool's specific role, so an agent can usually select correctly with careful reading.

Naming Consistency3/5

All names use snake_case and are generally descriptive, but they mix conventions: many are verb_noun (list_subscriptions, validate_claim), while others are noun phrases (entity_profile, recent_changes). Prefixes like oxylabs_ and polymarket_ are consistent, but the lack of a uniform verb-first pattern reduces predictability.

Tool Count2/5

With 34 tools, the server exceeds the 25-tool threshold for 'too many'. While the broad scope (data lookup, scraping, prediction markets, memory, subscriptions) warrants a larger surface, the sheer number makes it difficult for agents to quickly identify the right tool without extensive scanning.

Completeness4/5

The tool set covers the core domain thoroughly: question answering, deep research, entity resolution, comparison, validation, web scraping, prediction market analysis, memory management, and subscriptions. Minor gaps like limited e-commerce scraping beyond Amazon and no direct data-writing tools exist, but they do not critically hamper workflows.

Resources