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Montreal Events

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)

TDQS

A4.3/5.0
Behavior4/5

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

Annotations indicate idempotentHint=true, destructiveHint=false. The description elaborates on scoping (by identifier) and persistence (authenticated vs anonymous sessions with 24-hour expiration). No contradictions. Adds value beyond annotations by explaining data lifecycle.

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 front-loaded with purpose, followed by usage guidance, examples, and behavioral details. Every sentence contributes essential information. Slightly verbose due to enumeration of examples, but overall concise.

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?

For a simple write tool with no output schema, the description covers the key aspects: purpose, usage triggers, data scope, persistence, and companion tools. It does not describe return values or error handling, but those are less critical for a write operation.

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% with concise descriptions for 'key' and 'value'. The description provides naming conventions (e.g., 'subject_property', 'target_ticker') that add practical guidance beyond the schema. Since coverage is high, the extra examples lift the score above baseline 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's purpose: 'Save data the agent will need to reuse later.' It specifies the action (save), resource (data), and context (across conversation or sessions). The examples and pairing with siblings distinguish it from 'recall' and 'forget'.

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?

The description explicitly advises when to use: 'Use when you discover something worth carrying forward' with concrete examples. It also mentions pairing with 'recall' and 'forget' for lifecycle management. However, it does not explicitly state when not to use, leaving some ambiguity.

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

B3.4/5.0
Disambiguation2/5

The set contains multiple near-duplicate meta-query tools (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools) and five overlapping Polymarket analysis tools, creating genuine selection ambiguity despite detailed descriptions. The single events tool is distinct, but it is drowned out by a crowd of similar data-research utilities.

Naming Consistency4/5

Almost all tools follow a consistent lowercase snake_case verb_noun pattern (ask_pipeworx, compare_entities, validate_claim, remember, forget). Only 'events' deviates by being a bare noun, but the overall convention is predictable and readable.

Tool Count1/5

32 tools is extreme for a server named 'Montreal Events', and only one tool actually relates to that domain. The remaining 31 form a sprawling, unrelated data-research, prediction-market, and memory toolkit that would overwhelm any agent trying to work with Montréal event data.

Completeness1/5

For the server's stated purpose, the surface is severely incomplete: a single read-only event search with no event details, venue info, categories, or management operations. The Pipeworx tools may cover their own domain thoroughly, but they contribute nothing to the Montreal Events scope.

Resources