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Glama

SentimentFX

by org.sentimentfx

get_correlation

180-day Pearson correlation between daily sentiment shifts and next-day price returns for ticker.

Returns Pearson `r`, `p_value`, `n_days` overlapping, a 95% confidence
interval (Fisher z), and a categorical `strength` (strong / weak /
inconclusive).  Costs 1 API credit — same as GET /v1/correlation/{ticker}.

Requires ≥30 overlapping day-pairs.  Under that, returns a `note` field
explaining what's missing so a caller can suggest waiting or switching
to a higher-coverage ticker.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses the returned fields (r, p_value, n_days, CI, strength), the 1-credit cost, the minimum data requirement, and the edge-case behavior with a note field. It does not mention authorization or rate limits, but these are not prominent concerns for a read-only analytical endpoint.

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?

Four sentences, each purposeful: the first states the computation, the second lists output fields, the third notes cost and endpoint equivalence, and the fourth covers the data threshold and fallback. It is front-loaded and free of filler.

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?

Given a single-parameter schema, an existing output schema, and no annotations, the description covers the essential invocation context: what it computes, what it returns, the cost, and the required data volume. It does not explicitly describe when to prefer this over sibling analysis tools, but that gap is minor for a tool of this specificity.

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 compensates by referring to `ticker` as the subject of the correlation and by mentioning 'higher-coverage ticker' in the fallback explanation. The parameter is a single, self-explanatory identifier; the description adds just enough context about its role.

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, concrete statement: '180-day Pearson correlation between daily sentiment shifts and next-day price returns for `ticker`.' This names the exact computation, resource, and variables involved, making it immediately distinguishable from sibling tools like get_prices or get_sentiment.

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?

It provides clear context for when the tool is viable by requiring ≥30 overlapping day-pairs and explaining the fallback note when that threshold is not met. It does not explicitly contrast with alternative sibling tools, but the uniqueness of the correlation computation makes the usage context sufficiently clear.

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

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