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Kairos Signal — 63-Layer Symplectic Neural ODE

register_agent

START HERE. One call: get $5 free credits + API key, instantly (no card, no human, no Stripe, ~5 seconds). Works immediately: query live DePIN telemetry (457 symbols, every value carries a verify_url you can check yourself), GPU inference, DAG manifold. Try get_data_dictionary first if you want the coverage spec, or GET https://kairossignal.com/try with zero setup. Nothing to cancel; credits just sit there until you spend them. Then list_products to see what $0.49+ buys.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
emailNoOPTIONAL contact email (for delivery and topup notifications). Omit to register anonymously.
agent_nameYesYour agent name

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 the full burden. It discloses notable behavioral traits: no card or human required, instant (~5 seconds), no Stripe involvement, nothing to cancel, and credits that sit until spent. It stops short of describing the exact response format or idempotency, but it provides substantial behavioral context.

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 longer than strictly necessary, with some promotional phrasing, but it front-loads the purpose ('START HERE. One call...'), then adds value with alternatives, next steps, and behavioral reassurances. Each sentence contributes useful orientation even if some could be trimmed.

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 two-parameter registration tool with no output schema, the description covers what you get (free credits, API key), that it works immediately, and what to do next. The precise response format is not described, but the essential usage context and integration with sibling tools are present.

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%, so the baseline is 3. The tool description itself adds no parameter-specific meaning beyond the schema's own descriptions (email optionality is already documented in the schema). It neither subtracts nor adds significant value here.

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 action: 'START HERE. One call: get $5 free credits + API key, instantly' — a specific verb (get/register) with a distinct resource (credits + API key). It also differentiates itself from siblings by positioning as the entry point and directing users to list_products afterward.

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 says 'START HERE', establishing when to use this tool as the first step. It offers an alternative (get_data_dictionary for coverage spec, or a URL for zero-setup access) and suggests list_products as the next step. It could more explicitly state when not to use it, but the onboarding context is strong.

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

A3.7/5.0
Disambiguation4/5

Most tools target distinct actions (browse, query, purchase, top-up), but get_zk_provenance and verify_footprint both promise cryptographic verification for datasets/records and could easily be confused. fetch_dataset vs get_stats also has mild overlap, but the descriptions clarify querying records vs. aggregate statistics.

Naming Consistency5/5

All tools follow a consistent snake_case verb_noun pattern: check_balance, fetch_dataset, list_products, purchase_data, register_agent, etc. The verbs are action-oriented and the nouns correspond to the resource being handled.

Tool Count5/5

Ten tools cover registration, billing, product browsing, data querying, stats, and verification without feeling bloated. The count is typical for a marketplace/API server and each tool serves a concrete step in the user journey.

Completeness4/5

The core lifecycle is complete: register, view credits, browse products, buy, query, verify, and top up. Minor gaps exist around post-purchase management, such as listing owned subscriptions/entitlements or managing/rotating the API key, but agents can complete the main workflows without dead ends.

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