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by io.github.blackboxfoundry

options_history_atm_iv

Read-onlyIdempotent

Get the historical at-the-money implied volatility time series for a ticker. For each date, returns the strike closest to 50-delta and its IV. Default to call ATM IV but supports puts. Useful for VRP calculations, term structure, regime detection, and as a primary feature in directional/vol forecasting models.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tickerYesStock ticker
call_putNoCall (default) or Put
expirationYesExpiration date YYYY-MM-DD (pick the same expiration across dates for consistency)

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint false. The description adds meaningful behavioral context: it explains the strike selection rule (closest to 50-delta) and the default call/put behavior. This goes beyond the annotations and helps the agent understand the tool's semantics. No contradiction with annotations.

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 two sentences with no fluff. The core purpose is front-loaded, followed by a brief methodology and use-case list. Every sentence adds value, making it efficient and easy for an agent to parse.

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 the tool's simplicity (3 parameters, no output schema), the description covers the main aspects: what it returns (ATM IV time series), how it selects strikes, default behavior, and real-world applications. It does not detail the output format (e.g., units, data structure), but the use cases and methodology provide enough context for most agents.

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 coverage is 100%, so all three parameters (ticker, expiration, call_put) are already documented in the schema. The description reinforces the default for call_put ('Default to call ATM IV') but does not add new information about parameter meaning or format. This matches the baseline for high schema coverage.

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 a specific verb and resource: 'Get the historical at-the-money implied volatility time series for a ticker.' It further details the methodology (strike closest to 50-delta) and default (call vs put). This distinguishes it from sibling tools like options_history_chain or options_chain, which target different data types.

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 provides use cases ('VRP calculations, term structure, regime detection, and as a primary feature in directional/vol forecasting models') but does not explicitly state when to use this tool over alternatives or when not to use it. It implies relevance for vol analysis but lacks explicit routing guidance.

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

B3.2/5.0
Disambiguation2/5

Many tools overlap heavily across domains: caselaw_search vs court_case_search vs court_opinion_search, caselaw_citation_lookup vs court_citation_resolver, and a cluster of company due-diligence tools (company_trust_check, counterparty_risk_score, entity_dossier, issuer_diligence_dossier, kyb_aml_evidence_case_file) that all screen a company for sanctions/risk/standing. With 290 tools, an agent will frequently face multiple equally plausible choices for the same user intent.

Naming Consistency3/5

The vast majority of tools follow a clean domain-prefix + snake_case pattern (census_, eia_, fmcsa_, npi_, cfpb_, etc.), but there are notable exceptions: entity_resolve and resolve_entity are reversed duplicates, reg_search (Federal Register) sits next to reg_cfr_search (CFR) with confusingly similar names, and carrier_monitor_recheck deviates from the carrier_vetting_* family.

Tool Count1/5

290 tools is an extreme count under any rubric, far exceeding even the 50+ threshold for the lowest score. While the group-filtering mechanism and meta-tools like list_tool_groups and search_available_datasets mitigate the practical burden, the raw surface is still massively oversized for an agent to select from accurately and efficiently.

Completeness4/5

For a read-only data-aggregation server, coverage is remarkably comprehensive across 59 domains, and generic fallbacks like cdc_dataset_query, eia_series_lookup, fred_observations, and bls_series prevent most dead ends. Minor gaps exist (a single GitHub tool, demo-only property_lookup coverage, no write/update operations anywhere), but the stated data-access purpose is well served.

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