Skip to main content
Glama

Macrostrat

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

A5/5.0
Behavior5/5

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

Annotations already signal readOnly/idempotent/non-destructive, and the description adds substantial behavioral detail: multi-source fan-out, GDELT→GNews fallback on rate-limit/5xx, USPTO soft-fail due to API sunset, and the exact return structure (changes[], total_changes, pipeworx:// URIs). This goes well beyond the annotation baseline and fully discloses expected behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Though lengthy, the description is dense and well-structured. Every sentence adds unique value: example queries, source logic with fallbacks, parameter format details, return shape, and an alternative-tool pointer. It is front-loaded with purpose and proceeds logically, without redundancy.

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?

Given the tool's complexity (three data sources, fallbacks, flexible `since` parsing, return structure, and no output schema), the description is remarkably complete. It covers all invocation aspects and alternative use cases, making it fully contextual for an AI agent.

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 descriptions cover all three parameters, but the description adds valuable semantics: `since` accepts ISO or relative shorthand with concrete examples ('7d', '30d', '3m', '1y') and a recommended '30d'/'1m'; `value` is illustrated with ticker and CIK examples; `type` explicitly limited to 'company'. This enrichment aids agent selection and correct invocation.

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 explains that the tool returns a change feed for a company in a given time window, fanning out to SEC EDGAR, GDELT/GNews, and USPTO. It provides natural-language example queries and mentions the single parallel-call architecture. It also distinguishes itself from entity_profile, making 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?

The description offers explicit usage guidance with sample queries like "What's new with X" and "updates on Acme," and directly recommends an alternative: 'Use entity_profile instead when you want the static profile.' It also suggests typical windows ('30d' or '1m'), giving the agent clear context for when and how to use the tool.

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

All Macrostrat 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, e.g., geology tools vs. Polymarket tools vs. memory tools. The main ambiguity is between ask_pipeworx and ask_pipeworx_grounded, but their descriptions clearly differentiate them (grounded vs. casual). Overall, an agent can reliably select the right tool.

Naming Consistency3/5

Names use snake_case consistently, but the structure varies: some are verb_noun (find_columns), some are noun_noun (entity_profile), some are single verbs (forget). This mix reduces predictability, though each name is still readable.

Tool Count3/5

With 29 tools, the server covers many domains (geology, finance, prediction markets, memory, subscriptions). This is a large surface for a server named 'Macrostrat', which implies a geology focus. The count feels bloated for a coherent set, though each tool individually seems justified.

Completeness4/5

The tool set is quite complete for its diverse sub-areas: geology has lookup tools, Pipeworx/Poly market has search, comparison, arbitrage, and memory/subscriptions have full CRUD. Minor gaps exist (e.g., no detailed geology unit edits), but overall coverage is strong.

Resources