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Glama

Calendly

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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. Description adds significant behavioral detail: fans out to SEC, GDELT→GNews with fallback, USPTO sunset note, returns structured changes with citation 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?

Efficiently packs examples, source details, fallback logic, parameter tips, and sibling distinction into a dense paragraph. Slightly long but every sentence adds value; front-loaded with example queries.

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?

Covers core behavior, all data sources, return structure, and limitations (USPTO soft-fail). No output schema but explains return shape. Could mention potential empty results or error handling, but overall solid.

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 parameter descriptions. Description adds practical advice (e.g., typical monitoring period for since, ticker vs CIK for value) and query examples, going 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 clearly states the tool aggregates recent changes (filings, news, patents) for a company in one parallel call, with examples like 'What's new with X' and explicit distinction from 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 Guidelines4/5

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

Provides explicit when-to-use via alternative entity_profile, gives parameter advice ('Use 30d or 1m for typical monitoring'), and notes fallback behavior and limitations (USPTO soft-fail). Could be improved by clarifying when the tool returns empty or errors.

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

All Calendly toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

Several research tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route to the same underlying catalog. Similarly, bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, and polymarket_kalshi_spread form a dense prediction-market cluster with fuzzy boundaries. The few Calendly tools are distinct, but they are drowned out by duplicative families.

Naming Consistency3/5

Most names use snake_case and a noun-based pattern (e.g. polymarket_edges, entity_profile), and several follow verb_noun (list_event_types, list_scheduled_events, resolve_entity). But there are standalone verbs (forget, recall, subscribe) and inconsistent verb styles (ask_ vs list_ vs scan_), so the pattern is readable but not predictable.

Tool Count2/5

36 tools is far too many for a server ostensibly named Calendly: only 5 tools relate to Calendly events and invitees, while 31 tools belong to an unrelated Pipeworx data-research platform. The count is bloated by a second domain bolted onto the server, making the surface hard to navigate for its apparent purpose.

Completeness2/5

For the Calendly domain implied by the server name, the surface is missing core operations: there is no create/update/cancel/delete event, no cancel or reschedule flow, no invitee management beyond listing, and no event-type creation or editing. The Pipeworx side is broader, but even it has gaps like no bulk data export or direct raw-tool invocation.

Resources