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schwab_get_market_hours

Get trading hours for one or more market types.

Returns nested markdown — one block per market product Schwab knows about within the requested categories:

— OPEN | CLOSED Pre Market: YYYY-MM-DDTHH:MM — YYYY-MM-DDTHH:MM Regular Market: YYYY-MM-DDTHH:MM — YYYY-MM-DDTHH:MM Post Market: YYYY-MM-DDTHH:MM — YYYY-MM-DDTHH:MM

Sessions are emitted only when Schwab reports hours for them — a closed market on a weekend or holiday will have no session lines under it.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
dateNoISO date to check (e.g. "2026-03-15"). Defaults to today when empty. The response is for a single trading day — pass each date explicitly if you need a multi-day forecast.
npubNoRequired. Your Nostr public key (npub1...) for credit billing.
marketsNoComma-separated market types. Schwab supports "equity", "option", "bond", "future", "forex". Unknown types are silently dropped by Schwab.equity,option
dpop_tokenNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Without annotations, the description bears full transparency burden. It explains the nested markdown output format, the structure of each market block, indicates sessions are conditional on Schwab reporting data, and notes the response is for a single day. This adequately discloses behavior beyond a simple 'get'.

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 highly concise, starting with a clear one-line purpose, then a terse example of the output format. Each sentence adds value, and the structure is well-organized with bullet-like formatting. No wasted words.

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?

Given the tool's moderate complexity (multiple markets, sessions), the description fully covers output format and conditional behavior. An output schema exists (not shown) to document return values, so the description appropriately focuses on usage and formatting context. It is complete for an agent to invoke the tool correctly.

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 75%, and parameter descriptions in the schema are already detailed. The tool description adds one key piece: the response is for a single trading day (implying each date must be queried separately) and notes unknown market types are dropped. However, most parameter meaning is already in the schema, so added value is marginal.

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 'Get trading hours for one or more market types.' It uses a specific verb ('Get') and resource ('trading hours'), and uniquely identifies the tool among siblings. The example output further clarifies the purpose.

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?

No explicit guidance on when to use this tool versus alternatives is provided. The description implies use for checking market hours and explains output format, but lacks when-not-to-use or comparison with sibling tools. Usage context is inferred from the purpose.

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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