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Glama

Twilio

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

A4.3/5.0
Behavior4/5

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

Annotations already indicate readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false. The description adds valuable behavioral details: the tool fans out to multiple sources, has a fallback mechanism (GDELT→GNews), handles rate limits, and soft-fails for patents. This exceeds what annotations provide.

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?

The description is detailed but well-structured, starting with example queries. Each sentence adds meaningful information without redundancy. While slightly long, it remains focused and front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (three data sources, fallbacks, parameter formats) and the absence of an output schema, the description adequately covers return structure (grouped changes, total count, citation URIs) and usage context. Minor gaps: error behavior beyond rate limiting is not detailed.

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% with descriptions for all three parameters. The description adds further semantic value: clarifies 'since' accepts ISO dates or relative shorthand with examples, and 'value' can be ticker or CIK. This enhances understanding 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 clearly states the tool's purpose: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It lists specific data sources (SEC EDGAR, GDELT/GNews, USPTO) and distinguishes itself from sibling tool entity_profile, which provides 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 Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use examples ('What's new with X' etc.) and directs to entity_profile for static profile queries. However, it doesn't explicitly state when not to use this tool (e.g., for real-time data or non-company entities), though the context is clear.

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

B3.2/5.0
Disambiguation2/5

The server is named 'Twilio' but contains only 5 Twilio-specific tools mixed with 31 unrelated Pipeworx tools. An agent must distinguish between Twilio and Pipeworx functionality, and the tool descriptions are clear individually, but the overall set is confusing because the server name implies a focused Twilio service, not a general-purpose data platform with a few Twilio actions.

Naming Consistency2/5

The Twilio tools follow a consistent 'twilio_verb_noun' pattern (e.g., twilio_send_sms, twilio_make_call), while the Pipeworx tools use a separate snake_case convention (e.g., ask_pipeworx, entity_profile, deep_research). The two naming conventions are clearly distinct and do not mix, but the overall set is inconsistent because the server is named after one convention yet the majority of tools follow a different one.

Tool Count1/5

With 36 tools, the count is high, but the critical issue is that only 5 tools are relevant to the Twilio server name. The remaining 31 tools belong to Pipeworx, a completely different domain. This is an extreme mismatch between the claimed server purpose (Twilio) and the actual tool set, making the tool count inappropriate.

Completeness2/5

For a Twilio-focused server, the tool set is very incomplete: it covers only basic SMS sending, call initiation, and listing messages/calls. Missing are phone number management, media handling, conversation features, and other common Twilio operations. The Pipeworx tools are comprehensive for their own domain, but they are irrelevant to the Twilio server's stated purpose, leaving significant gaps.

Resources