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equibles

by io.github.daniel3303

Earnings Call Tone and Themes

GetEarningsCallToneAndThemes
Read-only

Get the AI-scored insights for a company's recent earnings calls — the management-tone read (a net tone score and a hedging score) and the call's key themes with their computed mention counts and per-theme tone. Newest call first. Verifier-approved — only scored and approved calls appear, so quarters can be missing from the sequence (a gap note flags non-consecutive quarters). Use it to gauge how confident or guarded management sounded and what they talked about most.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMaximum number of scored calls to return, newest first (default 2, max 8; values outside 1-8 are clamped)
tickerYesCompany ticker symbol (e.g., AAPL, MSFT)
fiscalYearNoCompany fiscal year. Omit both period fields for newest results; year alone filters that fiscal year.
fiscalQuarterNoCompany fiscal quarter, 1-4. Quarter alone filters that quarter across fiscal years; both fields select an exact period.

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds meaningful behavioral context beyond that: verifier-approved calls only, potential missing quarters with gap notes, and AI-scored nature of the data. It does not contradict annotations and enriches understanding of data quality and completeness.

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—three sentences—with the primary purpose stated upfront, followed by key data quirks and a usage hint. Every sentence adds value and there is no redundancy or filler.

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 tool with four parameters, full schema coverage, annotations covering safety, and a clear description of return content (tone scores, themes, mention counts), nothing essential is missing. The description even explains the gap-note behavior and ordering, making the tool fully navigable for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so all four parameters (ticker, limit, fiscalYear, fiscalQuarter) are already documented in the schema with meaningful descriptions. The tool description does not add any additional parameter-level detail beyond what the schema provides, so a baseline of 3 is appropriate.

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 clearly states the exact resource (company earnings calls), the specific outputs (net tone score, hedging score, key themes with mention counts and per-theme tone), and the ordering (newest first). It distinguishes itself from similar siblings like GetEarningsBrief and GetEarningsCallTranscript by focusing on AI-scored tone and themes.

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 on when to use the tool ('to gauge how confident or guarded management sounded') and explains data availability quirks (verifier approval, gaps in quarters). However, it does not explicitly name alternative tools or state conditions for selecting this one over others, leaving some routing inference to the agent.

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

B3.4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, with detailed descriptions that cross-reference related alternatives. A few near-duplicate names could cause misselection, notably SearchDocument versus SearchDocuments and GetCftcPositioning versus GetLatestCftcPositioning.

Naming Consistency5/5

Tool names consistently follow a VerbNoun camelCase pattern: Get for retrievals, Search for discovery, List/Read for document access, and Add/Close/Remove/Update/Watch/Create/Delete for portfolio mutations. Despite the large count, there is no mixing of naming conventions or unpredictable verb styles.

Tool Count1/5

108 tools is an extreme surface area, far beyond the 3-15 well-scoped range and well past the 25+ threshold. Even for a broad financial data platform, this creates a heavy selection burden and substantial context overhead for agents.

Completeness4/5

The server covers an unusually wide domain: prices, fundamentals, SEC filings, options, insider activity, 13F holdings, short interest, macro data, funds, IPOs, and full portfolio lifecycle management. Notable gaps remain, such as a basic company profile/ticker-resolution tool, dividend history, and analyst estimates, so it is not a perfect 5.

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