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Glama

Amtrak

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

A4.8/5.0
Behavior4/5

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

Annotations already indicate readOnly, openWorld, idempotent, non-destructive. The description adds context: fans out to multiple sources, fallback logic, soft-fail for USPTO, returns grouped changes with citation URIs. Slightly more detail on rate limits would enhance, but still strong.

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?

The description is compact but information-dense, starting with example queries, then purpose, sources, parameter details, and ending with sibling guidance. Every sentence contributes value without waste.

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 3 parameters, 100% schema coverage, no output schema, and complex multi-source aggregation, the description covers purpose, usage context, expected sources, fallback behavior, and output structure (changes grouped by source, total count, citation URIs). Complete for the use case.

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 description adds examples for `since` ('7d', '30d', '3m', '1y') and suggests typical monitoring values. It clarifies `value` accepts ticker or CIK, and `type` is only 'company'. Adds significant value beyond 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 explicitly states it provides a 'change feed for a company in the last N days/weeks/months in ONE parallel call,' gives example queries, and lists data sources. It distinguishes from the sibling 'entity_profile' by clarifying when to use the alternative.

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 provides clear guidance: use for dynamic changes over a window, and explicitly says to use entity_profile for static profiles. It also explains fallback behavior (GDELT→GNews) and soft-failures for USPTO.

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

A3.5/5.0
Disambiguation2/5

Many tools have overlapping purposes, e.g., ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded all answer questions; polymarket_edges and polymarket_arbitrage both find opportunities; and memory tools (remember, recall, forget) are separate but simple. This makes it hard for an agent to pick the right tool without careful reading.

Naming Consistency2/5

Naming is inconsistent: some tools use verb_noun (generate_llms_txt, discover_tools), others noun_noun (entity_profile, amtrak_station_info), and some are single verbs (remember, forget). The Amtrak tools follow a pattern but the rest do not, leading to a chaotic mix.

Tool Count2/5

With 35 tools but only 4 actually related to Amtrak, the server is severely over-scoped for its stated purpose. The majority of tools are unrelated, making it feel bloated and misnamed.

Completeness1/5

For an Amtrak server, the tool surface is severely incomplete: lacks booking, schedules, ticket info, delay details beyond worst delay, and station amenities. The tiny Amtrak subset is a mere fraction of what users would expect.

Resources