schwab-mcp

by psonhoang

Overview Schema Source Related Servers Discussions Admin

Python

Local

list_transactions

Fetch transaction history for a Schwab account by date range and type, with optional ticker filter.

Instructions

List transactions for one Schwab account within a date range.

Args: account_number: The plain Schwab account number as shown to the user (not the internal account hash). start_date: Start of the date range, inclusive (ISO-8601 date or datetime string). end_date: End of the date range, inclusive (ISO-8601 date or datetime string). types: Required Schwab transaction-type filter, e.g. "TRADE", "DIVIDEND_OR_INTEREST". symbol: Optional ticker symbol to further filter transactions.

Returns: A list of transaction objects.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`types`	Yes
`symbol`	No
`end_date`	Yes
`start_date`	Yes
`account_number`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 21, 2026

TDQS

A3.9/5.0

Behavior2/5

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

No annotations are provided, so the description carries the burden of behavioral disclosure. It states the operation and return type but is silent on pagination, ordering, error behavior, authorization prerequisites, or rate limits. The read-only nature is not explicitly disclosed.

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 well structured with a one-sentence summary followed by an Args section and a Returns line. Every line earns its place and adds information not present in the input schema.

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 has 5 parameterschery, no annotations, and no enum constraints, the description covers all required and optional parameters plus the return shape. It omits minor operational details like pagination and error handling, but the presence of an output schema reduces the need for describing return values.

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

Parameters5/5

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

Schema description coverage is 0%, so the docstring must compensate, and it does thoroughly. It explains each parameter in plain terms, including the account_number nuance (plain number vs internal hash), inclusive ISO-8601 dates, required types filter with examples, and optional symbol filtering.

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 opening sentence names a specific verb and resource: 'List transactions for one Schwab account within a date range.' This clearly distinguishes the tool from siblings like get_transaction (single transaction) and list_orders (orders).

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 implies when to use the tool through its scope ('one Schwab account', 'date range'), but it does not explicitly mention alternatives or provide exclusion conditions, such as using get_transaction for a single transaction. It provides clear context but no direct routing between siblings.

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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