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LiveDataLink

by io.github.blackboxfoundry

options_history_contract

Read-onlyIdempotent

Track a specific options contract (same ticker + strike + expiration) over time. Returns all daily observations of that contract with its evolving bid/ask, IV, and Greeks. Useful for studying single-contract behavior leading up to expiration, gamma squeezes around specific strikes, etc.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
strikeYesStrike price (e.g. 450.00)
tickerYesStock ticker
call_putYesContract type
expirationYesExpiration date YYYY-MM-DD

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already establish that the tool is read-only, idempotent, and non-destructive. The description adds behavioral context by stating that all daily observations are returned with evolving bid/ask, IV, and Greeks, which clarifies what the call produces beyond the annotation metadata.

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?

Three sentences each serve a clear purpose: defining the tool, describing the return content, and providing use cases. It is front-loaded with the core action and contains no redundant or filler language.

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?

Even without an output schema, the description tells the caller what data comes back and why they would use it. All four required parameters are fully documented in the schema, and the historical daily-observation framing is sufficient for a simple read-only tool. Minor gaps like time range or pagination are not critical for correct invocation.

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%, with every parameter already documented, including examples for strike and expiration. The description restates the contract identity concept but adds no meaningful parameter-level information beyond what the input schema provides.

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, 'Track,' and names the exact resource: a single options contract identified by ticker, strike, and expiration. It clearly scopes the tool to one contract rather than a chain, which distinguishes it from sibling tools like options_history_chain without requiring schema inspection.

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?

The description gives explicit use cases: studying single-contract behavior leading up to expiration and gamma squeezes around specific strikes. It does not name alternative tools or state when not to use this tool, so it lacks explicit exclusions but still conveys clear intended usage.

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.

Resources