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Glama

cocktails

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

A4.6/5.0
Behavior5/5

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

Despite annotations already declaring readOnlyHint, idempotentHint, and non-destructive behavior, the description adds operational depth: SEC EDGAR and USPTO sources, GDELT→GNews fallback on rate-limit/5xx, PatentsView API sunset with soft-fail, `since` format handling, and return structure. This goes well beyond what annotations alone convey.

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 dense and well-structured: user intents → purpose → source fan-out → date format → return structure → alternative. Each sentence carries value, but the quoted example intents are somewhat repetitive and could be trimmed. Still, it is front-loaded and scannable.

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?

With no output schema, the description takes on the burden of explaining return values, and it does: 'Returns structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs.' It also covers edge cases like source fallback and soft-fail for patents, plus the alternative tool. Given the multi-source complexity, this is complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100% (context signals), with each parameter well-described (type enum, since formats, value examples). The tool description largely restates the `since` format and value examples already in the schema, adding no new per-parameter meaning. Baseline 3 is appropriate.

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 is a change feed for a company, aggregating SEC filings, news (GDELT/GNews), and patents. It includes example user intents ('What's new with X', 'latest on Y') and explicitly distinguishes it from entity_profile, which is recommended for static profiles. The verb 'fans out' and the source enumeration make the purpose 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?

It explicitly provides when-to-use guidance through example query phrases and describes the tool as the right choice for 'change feed for a company in the last N days/weeks/months.' It also names a concrete alternative: 'Use entity_profile instead when you want the static profile...', which satisfies the 'when-not' criterion.

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

A3.7/5.0
Disambiguation2/5

There are several tools with overlapping purposes, especially the ask_pipeworx family (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the multiple Polymarket analysis tools (polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk) which all deal with market edge/arbitrage in slightly different ways. The cocktail tools are distinct, but the majority of the server is a general data lookup service, making it hard for an agent to quickly choose among the many similar 'research' tools.

Naming Consistency2/5

Tool names follow mixed conventions: some use verb_noun (get_cocktail, search_cocktails, list_subscriptions), some are noun phrases (entity_profile, bet_research, ai_visibility_check), and some are bare verbs (forget, recall, remember) or awkward compounds (polymarket_arbitrage, scan_competitor_ai_presence). There is no consistent pattern across the set, making predictable naming unreliable.

Tool Count2/5

At 35 tools, the count is high, and the server's stated purpose (cocktails) is completely mismatched with the vast majority of tools, which are about data research, prediction markets, and subscriptions. The cocktail functionality is only 4 tools, while the rest feels like a separate general-purpose data API, so the set is both over-scoped and incoherently bundled.

Completeness4/5

For the cocktail sub-domain, the tools cover the typical use cases: search by name, get full recipe by ID, list by ingredient, and random cocktail. There's no way to create or update recipes, but that's not expected for a read-only cocktail database. For the broader data/lookup purpose implied by most tools, the coverage is extensive, including routing, grounded answers, entity resolution, and comparison, so no critical dead ends are apparent.

Resources