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Glama

SentimentFX

by org.sentimentfx

get_sentiment

Return the most recent FinBERT-scored headlines for ticker.

Each headline has a `sentiment_score` in [-1, +1] (positive_prob -
negative_prob) and a categorical `sentiment_label`.  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.  Titles come back reverse-chronological so the
first item is the freshest.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
tickerYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.4/5.0
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 does so well: it discloses the scoring model (FinBERT), the score range and formula, categorical labels, billing behavior ('Costs 1 API credit per 25 headlines actually returned'), free empty results, the limit cap, and reverse-chronological ordering. This is substantial behavioral context beyond a simple 'returns data' statement.

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 compact and front-loaded: purpose first, then return semantics, billing, and constraints. Every sentence adds relevant information, with no filler or repetition of obvious schema facts.

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 tool with an output schema, the description covers the core call semantics, return fields, ordering, limit behavior, and cost. It falls slightly short by omitting failure/error behavior and any explicit alternative routing, but nothing critical is missing for a basic successful call.

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 description coverage is 0%, so the description must compensate. It does: `ticker` is identified as the subject of the headlines, and `limit` is given meaning through the cap of 100 and the per-25 billing note. It could go further by explicitly stating that `limit` controls the maximum number of headlines returned, but the inference is strong.

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 opens with a specific verb and resource: 'Return the most recent FinBERT-scored headlines for `ticker`.' This clearly distinguishes the tool from siblings like get_prices or get_summary by naming the exact resource type and its sentiment focus.

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 first sentence implies when to use the tool: whenever a caller wants sentiment-scored headlines for a ticker. However, it gives no explicit when-not-to-use guidance or contrast with sibling tools, so usage context is implied rather than explicitly routed.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All SentimentFX toolsInspect
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TDQS

A4.6/5.0
Disambiguation5/5

Each tool targets a distinct resource: raw headlines, daily aggregates, prices, correlation, usage, and ticker universe. Even the two sentiment-adjacent tools (get_sentiment vs get_summary) are clearly separated by granularity and response shape.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern: get_correlation, get_prices, get_sentiment, get_summary, get_usage, list_tickers. The one list_ tool is a conventional collection enumeration and does not break the predictability.

Tool Count5/5

Six tools is well-scoped for a read-only sentiment/price data API. Each tool provides a distinct, necessary capability with no redundancy or bloat.

Completeness5/5

The set covers the full read-only workflow: discover tickers, fetch prices, fetch raw headlines, fetch daily sentiment aggregates, compute the correlation between sentiment and returns, and check API usage. There are no obvious missing operations for the stated domain.

Resources