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Predictit

by io.github.pipeworx-io

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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

A4.6/5.0
Behavior5/5

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

Discloses persistence behavior (authenticated vs anonymous, 24-hour retention) and scope by identifier, adding significant value beyond annotations. 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.

Conciseness4/5

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

Every sentence adds value, but could be slightly more concise. Front-loaded with main action and context.

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?

Fully covers purpose, parameters, usage, and storage behavior with no gaps given the tool's simplicity.

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 descriptions already cover key and value; description adds usage context (example key patterns) and clarifies the key-value storage model.

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 sessions and distinguishes from siblings like recall and forget via explicit pairing language.

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?

Provides explicit when-to-use guidance ('discover something worth carrying forward') and mentions companions (recall, forget). Does not include when-not-to-use but is sufficient.

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

A4.1/5.0
Disambiguation4/5

Most tools have distinct purposes, but pairs like ask_pipeworx/ask_pipeworx_grounded and bet_research/polymarket_edges could cause confusion without careful reading. Overall, descriptions are clear enough to differentiate.

Naming Consistency3/5

Names are snake_case and mostly follow verb_noun pattern, but several are noun_noun (entity_profile, pipeworx_feedback, polymarket_arbitrage) creating inconsistency. Still readable due to descriptive terms.

Tool Count4/5

33 tools is slightly high but justified given the broad scope (data retrieval, prediction markets, memory, subscriptions). Each tool serves a specific role, so the count feels appropriate for the platform's capabilities.

Completeness4/5

The tool set covers data retrieval, prediction market analysis, memory management, and subscriptions well. Minor gaps exist (e.g., no direct betting tool), but core workflows are supported comprehensively.

Resources