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Stelar Signals

by io.github.StelarDigital
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Crypto / text sentiment scorer

crypto_sentiment
Read-only

Sentiment score (-1 to +1) with a bullish/neutral/bearish label, for either raw text or a crypto asset's recent price action, via an LLM. Give it EXACTLY ONE of text (free-form text to score) or asset (one of SOL, XLM, BTC, ETH, XRP, DOGE, LTC, ADA). Useful for agents building trading signals, news pipelines, or social-listening tools without running their own LLM sentiment call.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "crypto_sentiment"
  }
}

Input Schema

NameRequiredDescriptionDefault
textNoFree text to score (omit if using `asset`)
assetNoAsset symbol to score sentiment for (omit if using `text`)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.6/5.0
Behavior3/5

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

Annotations already mark the tool as read-only and non-destructive, so the bar is lower. The description adds that scoring is 'via an LLM', which implies external AI processing and possible nondeterminism/cost, but provides no detail on rate limits or latency. This is adequate given the annotation coverage.

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 compact and front-loaded, stating the output format and input modes before the use-case sentence. It is free of filler and every sentence contributes useful information, though the final use-case sentence is slightly promotional rather than strictly operational.

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 read-only tool with no output schema, the description covers the return format (score range and label), both input modes, the supported asset list, and the exclusivity constraint. The main vagueness is what 'recent price action' means, but this is acceptable for a lightweight sentiment scorer.

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 both `text` and `asset` fully described and the asset enum documented. The description adds critical value by explicitly enforcing 'EXACTLY ONE of `text` or `asset`', a mutual-exclusion constraint not encoded in the schema since both are optional.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool produces a sentiment score (-1 to +1) with a bullish/neutral/bearish label for either raw text or crypto asset price action. It identifies the verb, resource, and output format, but does not explicitly distinguish itself from the sibling `sentiment` tool, leaving some overlap ambiguity.

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

Usage Guidelines3/5

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

The description gives use cases (trading signals, news pipelines, social-listening) and the exactly-one-of-text/asset constraint, which implies appropriate usage. However, it never names alternative sibling tools or states when not to use this tool, so the when-to-use guidance remains implicit rather than explicit.

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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