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Glama

Emoji

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

A4.9/5.0
Behavior5/5

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

Annotations already mark it as read-only, non-destructive, idempotent, and open-world. The description adds significant behavioral context: parallel fan-out to multiple sources, GDELT→GNews fallback, USPTO soft-fail, and return structure including total_changes and citation URIs. No contradictions 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?

The description is a single paragraph that front-loads with example queries before explaining sources and parameters. Every sentence adds value, though it is dense and could be slightly more concise by separating the parameter explanation into a clearer list or shorter sentences.

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?

Despite lacking an output schema, the description mentions the return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs). It covers all critical aspects: purpose, data sources, fallback behavior, parameter guidance, and differentiation from sibling tool. For a multi-source aggregation tool, this is highly complete.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds substantial value: explains `since` with examples ('2026-04-01', '7d', '30d') and recommends '30d' or '1m'. Clarifies that `value` accepts ticker or CIK. Describes that `type` is currently limited to 'company'. This enables correct parameter usage beyond the schema's basic descriptions.

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, GDELT/GNews, USPTO) and example queries. It explicitly distinguishes itself from sibling tool 'entity_profile', fulfilling the distinction requirement.

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 provides when-to-use examples (e.g., 'latest on Y') and when-not-to-use (use entity_profile for static profile). Also explains fallback behavior for news sources, offering clear guidance on selecting this tool over alternatives.

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

Several tool groups have heavy functional overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical to stable right now), ask_pipeworx_grounded, deep_research, validate_claim, discover_tools, and suggest_questions all route around the same data-querying core. The Polymarket family (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) also blurs together, and ai_visibility_check vs scan_competitor_ai_presence are near-duplicates.

Naming Consistency4/5

Most tools follow a clear lowercase snake_case convention with family prefixes like ask_pipeworx_*, polymarket_*, and pipeworx_*. Minor deviations exist — bare verbs like remember/recall/forget and the quirky generate_llms_txt — but the overall pattern is predictable and readable.

Tool Count2/5

33 tools is already on the heavy side, but the real problem is scope: the server is named 'Emoji' yet only 2 of 33 tools relate to emoji, with the other 31 forming a sprawling data-research/prediction-market platform. The count feels mismatched with the server's apparent identity.

Completeness2/5

The data-research side is impressively broad, but the stated domain (Emoji) is barely covered — only lookup and keyword search with no listing, metadata, or classification features. The toolset is a grab bag of unrelated domains, so the overall surface is not complete for any single coherent purpose.

Resources