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Glama

Open Notify

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 indicate readOnly, openWorld, idempotent, non-destructive. Description adds valuable behavioral context: parallel fan-out to multiple sources, GDELT→GNews fallback logic, USPTO soft-fail, and citation URI structure. No contradictions.

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 comprehensive but slightly dense. It front-loads example queries, then technical details. Every sentence adds value, but could be split into clearer sections. Still highly informative without fluff.

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?

Despite no output schema, the description adequately explains return structure (grouped changes, total count, citation URIs). It covers use cases, data sources, and fallbacks. For a multi-source aggregation tool, this is thorough enough for an agent to understand behavior and results.

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%. Description enriches parameter meaning: explains `type` is 'company' only, gives `since` format examples with typical usage, and clarifies `value` accepts ticker or CIK. Adds practical guidance 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 specifies the tool's purpose: retrieving recent changes (SEC filings, news, patents) for a company. It uses clear verbs like 'change feed' and distinguishes from sibling `entity_profile` by contrasting dynamic vs. 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?

Description provides explicit guidance on when to use (change queries) and offers a clear alternative (`entity_profile`) for static profiles. It lacks explicit exclusion scenarios but implies usage through examples.

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

All Open Notify toolsInspect
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TDQS

A3.6/5.0
Disambiguation2/5

The three ask_pipeworx variants are near-identical (the beta is currently an exact copy of the stable router), and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all route natural-language questions to the same underlying source catalog. The six polymarket_* tools also heavily overlap in opportunity detection, though some clusters like memory and subscriptions are clearly separated.

Naming Consistency3/5

The naming is mostly snake_case but mixes conventions: verb_noun (list_subscriptions, resolve_entity), noun-first (entity_profile, bet_research), metadata-style prefixes (pipeworx_trending, polymarket_edges), and bare verbs (remember, recall, forget). It is readable but does not follow one predictable pattern across the set.

Tool Count2/5

33 tools is excessive for a server nominally named Open Notify, and the count is inflated by redundant ask_pipeworx variants and six closely-related Polymarket tools. The broad data-gateway scope could justify a large catalog, but the set feels bloated and unfocused rather than deliberately scaled.

Completeness2/5

For the apparent Open Notify domain, only astros and iss_now fit, and core ISS functionality like pass predictions is missing. The wider data-lookup surface is extensive, but the inclusion of unrelated memory, subscription, npm-scanning, and llms.txt tools means no single domain gets coherent lifecycle coverage.

Resources