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Glama

Amtrak

by io.github.pipeworx-io

Remember

remember
Idempotent

Save data the agent will need to reuse later — across this conversation or across sessions. Use when you discover something worth carrying forward (a resolved ticker, a target address, a user preference, a research subject) so you don't have to look it up again. Stored as a key-value pair scoped by your identifier. Authenticated users get persistent memory; anonymous sessions retain memory for 24 hours. Pair with recall to retrieve later, forget to delete.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keyYesMemory key (e.g., "subject_property", "target_ticker", "user_preference")
valueYesValue to store (any text — findings, addresses, preferences, notes)

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.7/5.0
Behavior5/5

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

Description adds key behavioral details not in annotations: scoped by identifier, persistence differences between authenticated (persistent) and anonymous (24-hour) sessions, and idempotent nature implied by 'save' – no contradiction.

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?

Three sentences with no fluff, front-loaded with purpose, then usage guidance, then behavioral details. Every sentence adds value.

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 simple params, no output schema, and rich annotations (idempotentHint true), description provides all needed context: scope, persistence, pairing with recall/forget.

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 covers both parameters with descriptions; description adds naming conventions (e.g., 'subject_property') and clarifies key-value storage, going beyond schema but not required.

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 verb 'Save' and the resource 'data the agent will need to reuse later', and distinguishes itself from sibling tools 'recall' and 'forget' by indicating pairing.

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?

Explicitly says 'Use when you discover something worth carrying forward' and references related tools 'recall' and 'forget', but does not explicitly state when not to use it.

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