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get_trading_signals

CORTEX trading signals - AI-generated direction, confidence score, and win rate for Solana tokens.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tokenNoFilter by specific token mint address

TDQS

A3.5/5.0
Behavior3/5

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

With no annotations, the description bears the full responsibility for behavioral disclosure. It states that the output includes AI-generated direction, confidence, and win rate, which is useful context, but it does not mention side effects, latency, authentication, or the behavior when no token filter is provided. The read-only nature is only implied by the name.

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 a single, concise phrase that is front-loaded with the tool's purpose. It contains no wasted words and directly communicates the core value proposition.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With only one optional parameter and no output schema, the description should clarify the default return behavior and response shape. It names the output fields but does not explain what happens when the token filter is omitted or whether the result is a list or single object. This makes it minimally adequate but with clear gaps.

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?

The input schema already describes the 'token' parameter as a filter by mint address with 100% coverage. The description adds no extra meaning beyond what the schema provides, so the 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 identifies the resource (trading signals) and specifies the key attributes (direction, confidence score, win rate) for Solana tokens. The verb is implied by the tool name 'get', and the content distinguishes it from siblings like competitor intel or market regime.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance is provided on when to use this tool versus alternatives. The description is purely definitional and does not mention any exclusions, prerequisites, or when one might prefer a sibling tool.

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
Disambiguation5/5

Each tool targets a distinct domain: competitor intel, market regime, social trends, trading signals, token risk analysis, server health, and tool discovery. Even scan_token and scan_token_preview are clearly differentiated as full analysis vs. quick preview.

Naming Consistency3/5

Four tools follow a consistent get_* pattern, and two follow scan_* pattern, but 'health' and 'list_tools' break the convention. Mixed verb styles and one bare noun reduce consistency.

Tool Count5/5

Eight tools is well-scoped for a specialized crypto intelligence server, covering market analysis, social trends, competitor intel, and risk scanning without unnecessary bloat.

Completeness4/5

The core lifecycle of intelligence gathering and risk assessment is covered, including full and preview token scans. Minor gaps exist (e.g., no direct token metadata endpoint), but agents can work around them with existing tools.