Skip to main content
Glama

SentimentFX

by org.sentimentfx

get_summary

Return daily aggregated sentiment for ticker over the last days.

Each entry has `avg_sentiment` (unweighted mean of that day's scores),
`article_count`, and a directional `label` (positive / negative /
neutral, thresholded at ±0.1).  Costs 1 API credit per day actually
returned — same as GET /v1/summary/{ticker}.  A window with no coverage
costs nothing.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNo
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

A4.3/5.0
Behavior4/5

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

With no annotations, the description carries the full transparency burden. It goes beyond a basic summary by specifying the output fields, the ±0.1 label threshold, an identical REST endpoint, and a per-day credit cost including the no-coverage case. It does not address auth or error behavior, but it gives substantial behavioral detail.

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 well front-loaded: purpose first, then output semantics, then cost. Every sentence adds information; there is no repetition of schema titles or boilerplate.

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 two-parameter read-style tool with an output schema, the description explains inputs, output shape, threshold behavior, and cost edge case. There is no missing information an agent would need to decide whether and how to invoke it.

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?

The schema has 0% description coverage, so the description must define both parameters. The first sentence maps ticker to the instrument and days to the trailing window, and the default is visible in the schema. This is sufficient for the two simple parameters, though it leaves numeric constraints like max days unstated.

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: returning daily aggregated sentiment for a ticker over a window. This clearly differentiates it from sibling tools like get_prices and get_sentiment by emphasizing the daily aggregation level.

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 implies when to use the tool—when daily aggregated sentiment is needed—and the cost rule adds practical context. However, it does not explicitly name sibling get_sentiment or state when to choose raw vs aggregated sentiment, leaving the alternative-selection logic to the agent.

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

All SentimentFX toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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