Skip to main content
Glama

marine

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

A4.5/5.0
Behavior4/5

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

Annotations already cover read-only and idempotent hints. The description adds valuable behavioral details: parallel fan-out, GDELT→GNews fallback on rate-limit/5xx, USPTO sunset soft-fail, and return structure. These go beyond annotations without contradicting them.

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 but each clause earns its place: examples, source details, fallback logic, API sunset caveat, parameter formats, and return summary. It is front-loaded with purpose, though slightly long, it remains efficient.

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?

No output schema exists, but the description summarizes return fields (changes[], total_changes, citation URIs) and source behavior. Given the tool's multi-source complexity, it covers essential aspects, though it lacks example output shapes or pagination details.

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%, so baseline is 3. The description adds a recommended usage ('30d' or '1m' for typical monitoring), clarifies that `since` controls the window across all sources, and reinforces value formats, providing marginal extra meaning.

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 identifies the tool as a 'change feed for a company in the last N days/weeks/months' with specific sources (SEC EDGAR, GDELT/GNews, USPTO) and explicitly contrasts with the sibling entity_profile, making the purpose and resource unambiguous.

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?

Provides explicit query patterns ("What's new with X") and an explicit alternative: 'Use entity_profile instead when you want the static profile...'. This gives clear when-to-use and when-not-to-use guidance.

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

All marine toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but some overlap exists in the prediction market area (e.g., bet_research vs. polymarket_edges vs. polymarket_arbitrage) and in AI visibility checks. Descriptions help differentiate, but confusion is possible.

Naming Consistency3/5

Names mix verb_noun (ask_pipeworx, generate_llms_txt) and noun_verb patterns (entity_profile, polymarket_edges). While some groups are consistent individually, the overall naming lacks a uniform convention.

Tool Count4/5

With 32 tools covering a broad domain (data lookup, prediction markets, memory, subscriptions), the count is slightly high but still reasonable for the scope. Each tool has a specific role.

Completeness4/5

The tool set is comprehensive for the stated domain, including data retrieval, entity analysis, comparisons, research, and prediction markets. Minor gaps exist (e.g., no direct CRUD for user data besides memory), but the surface is mostly complete.

Resources