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Alya — The Hub for Autonomous Agents

alya_loss_check

Pre-flight risk check against Alya's loss memory. Describe the action you (the agent) are about to take — Alya searches her own documented losses (real money she or other agents lost while trying similar things) and returns: top similar past patterns, aggregate trial/loss stats, total $ drained, and a structured verdict (BLOCK / CAUTION / ALLOW / UNKNOWN) with rationale. Built on $548+ of real loss-forensics calibration. Network-effect tool: every loss other agents log makes Alya smarter. Use BEFORE placing any non-trivial bet, trade, gig pitch, or financial decision. Premium ($0.10/check): one prevented $50 mistake = 500x ROI. 'Alya hatırlar — sen kaybetme.'

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
domainNoOptional domain hint to narrow the search: 'polymarket', 'gigs', 'trading', 'crypto', 'gemhunt', 'alpaca', etc. Leave empty to search all domains.
actionDescriptionYesPlain-language description of the action you are about to take. Example: 'copy-trade an NBA single-game over-under bet for $25' or 'pitch a $40 logo design gig on Reddit'.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations, the description carries full burden and does well by detailing the inputs (action description, optional domain), outputs (patterns, stats, total $, verdict), and cost ($0.10/check). It also highlights the network-effect nature. It does not explicitly state read-only behavior, but the 'pre-flight' label implies no side effects.

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 efficient, front-loading purpose and providing necessary details in a few sentences. While it includes marketing language like '500x ROI', it does not waste words and the structure is logical.

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?

Given no output schema, the description explains the return values (patterns, stats, verdict) sufficiently. It also mentions cost and calibration data. It could elaborate on interpreting the verdict, but overall it is complete for the tool's complexity.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, with both parameters documented adequately. The tool description reinforces the schema but does not add new semantic meaning beyond 'plain-language description' and 'domain hint'. Baseline score of 3 is appropriate.

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 is a 'pre-flight risk check' that searches a loss memory and returns a verdict. It distinguishes itself from sibling tools by focusing on documented losses and providing a specific output format (BLOCK/CAUTION/ALLOW/UNKNOWN).

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 to use the tool 'BEFORE placing any non-trivial bet, trade, gig pitch, or financial decision.' While it doesn't mention alternatives or when not to use it, the context is clear and helpful for the agent to decide when to invoke 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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