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options_chain

Read-onlyIdempotent

Get the options chain for a stock - calls and puts with strike prices, bid/ask spread, volume, open interest, implied volatility, and available expirations. Use this for "show me AAPL options", "what are the puts on Tesla?", "options expiring this Friday", "what's the implied volatility?", or any options trading question.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeNoFilter by option type (default: "both")
symbolYesStock ticker symbol (e.g., "AAPL")
expirationNoExpiration date in YYYY-MM-DD format. Defaults to nearest expiration.

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 non-destructive behavior, so the safety profile is covered. The description adds useful behavioral context by specifying exactly what data is returned (calls, puts, strike prices, bid/ask, volume, open interest, implied volatility, expirations), going beyond what the schema alone provides.

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 efficient: the first sentence states the core function and key outputs, and the second provides concrete trigger examples. Every sentence earns its place with no filler or redundant restatement.

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?

For a simple read-only tool with fully documented parameters, the description covers the essential return fields and gives practical usage examples. It loses a point because the closing phrase 'or any options trading question' is overly broad given the many options-related sibling tools, which could steer an agent toward this tool in ambiguous cases.

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 the input schema fully documents symbol, type, and expiration. The description's examples add natural-language mappings for the parameters, but they do not add meaningful structural or format details beyond the schema, so the baseline of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states a specific verb-resource pair ('Get the options chain for a stock') and lists the returned data fields, making the tool's purpose obvious. However, it does not explicitly distinguish this from the sibling options_history_chain or other options-related tools, so it falls just short of full sibling differentiation.

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 'Use this for' section gives concrete example queries such as 'show me AAPL options' and 'what are the puts on Tesla?', providing clear context for when an agent should select this tool. It does not mention when not to use it or name alternatives like options_history_chain, so it lacks explicit exclusions.

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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