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SentimentFX

by org.sentimentfx
OverviewDiscussionsAdmin

get_sentiment

Return the most recent FinBERT-scored news headlines for ticker.

Each headline has a `sentiment_score` in [-1, +1] (positive_prob -
negative_prob), a categorical `sentiment_label`, and a `source_kind`
(news / reddit / stocktwits / x / youtube).  Costs 1 API credit per 25
headlines actually returned — same billing as GET /v1/sentiment/{ticker}.
An empty result costs nothing.

`limit` is capped at 100.  Social posts are kept separate from news:
the default `sources="news"` returns editorial news, `"social"` returns
only Reddit, StockTwits and X posts, and `"all"` returns both, labelled
by `source_kind`.  Titles come back reverse-chronological so the first
item is the freshest.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "ticker": "<ticker>"
    },
    "name": "get_sentiment"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNo
tickerYes
sourcesNonews

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / sources
      Added value: +{
+  "default": "news",
+  "enum": [
+    "news",
+    "social",
+    "all"
+  ],
+  "title": "Sources",
+  "type": "string"
+}
  2. First observed

TDQS

A4.6/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and delivers: billing model ('1 API credit per 25 headlines actually returned', empty results free), a hard cap on `limit` at 100, and reverse-chronological ordering. These are exactly the operational facts an agent needs before calling.

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?

Front-loads the core purpose, then layers billing, caps, and source semantics in short scoped sentences. Slightly over-detailed for the response fields (sentiment_score formula) which the output schema already covers, but nothing is wasted.

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?

For a 3-parameter read tool with an output schema present, the definition is fully sufficient: it covers cost, limits, source partitioning, and ordering, and even enumerates return fields so an agent can reason about results.

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 0%, so the description must compensate: it documents the `limit` cap of 100, the three `sources` enum values with their default behavior, and what each returned `source_kind` means. `ticker` is only implied, but its meaning is self-evident.

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?

States a specific verb and resource ('Return the most recent FinBERT-scored news headlines for `ticker`') plus the scoring methodology, which is enough for an agent to distinguish it from get_prices, get_correlation, and get_summary without opening the schema.

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?

Explains how to choose between the `sources` modes ('news' editorial vs 'social' Reddit/StockTwits/X vs 'all'), giving clear selection context for the main behavioral choice. It does not, however, say when to prefer this tool over siblings like get_summary.

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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