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Glama

Irail

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

A4.9/5.0
Behavior5/5

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

Adds value beyond annotations by specifying scoping by identifier, persistence differences for authenticated vs. anonymous sessions, and storage format (key-value). No contradictions with annotations.

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 concise, front-loaded with the main action, and uses efficient sentences without redundancy. 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?

For a simple two-parameter tool with no output schema, the description covers all needed context: purpose, usage, persistence details, and related tools. Completeness is high.

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 has 100% coverage with descriptions for key and value. The description adds examples of key patterns (e.g., 'target_ticker') that enhance understanding, justifying a score above the baseline of 3.

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 tool saves data for reuse across conversations/sessions, using specific verbs and resource framing. It distinguishes from sibling tools like recall and forget by explicitly mentioning pairing with them.

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?

Explicitly says to use when discovering something worth carrying forward, and pairs with recall/forget for retrieval/deletion. Provides clear context for when to use vs. alternatives.

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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions. However, the three ask_pipeworx variants (standard, beta, grounded) and the five polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) have overlapping functionality that could confuse an agent if descriptions are not carefully parsed. The memory tools (remember, recall, forget) are clear. Overall, the set is mostly disambiguated.

Naming Consistency3/5

Tool names use snake_case consistently, but naming patterns vary: some follow verb_noun (e.g., ai_visibility_check, compare_entities), others are noun_noun (e.g., bet_research, entity_profile), and a few are single verbs (e.g., recall, remember, forget, subscribe). This mixed pattern reduces predictability, though the names are still readable.

Tool Count2/5

At 35 tools, the server is overly broad, covering Belgian rail, Pipeworx data queries, prediction markets, memory, subscriptions, and more. The server name 'Irail' suggests a focused rail toolset, but rail is only a small part. This scope mismatch and high tool count make it feel bloated and less coherent.

Completeness4/5

Within each subdomain, the tool surface is fairly complete. Belgian rail has journey planning, liveboard, train tracking, and disturbances. Data querying has universal, grounded, deep research, entity profiles, comparisons, recent changes, and claim validation. Prediction markets have arbitrage, edge detection, edge tracking, fill risk, and cross-venue spread. Memory and subscriptions are covered. Minor gaps exist (e.g., no tool for deleting entities or canceling orders), but overall coverage is strong.

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