Moby

My DCA schedules

get_my_dca_schedules

Read-only

The user's DCA schedules — recurring buys or sells that split an amount into slices over time — with progress so far (how many slices have filled and when the next one is due). The same rows get_my_orders returns as dca_schedules; use this one when you only need the DCAs. get_my_orders answers 'what do I have running' in ONE call: orders (limit, take-profit, stop-loss) plus dca_schedules (DCA schedules, one row per schedule; a DCA's individual slices never appear in orders). get_my_dca_schedules is the same DCA rows on their own. If dca_schedules is null, the DCA read failed (dca_schedules_error): call get_my_dca_schedules before telling the user they have no DCA running. By default only live schedules are returned; set include_terminal to also see finished and cancelled ones. To stop a whole DCA, pass its schedule_id to cancel_order. This returns the CURRENT USER's own data, resolved from their API key. There is no way to ask about someone else's wallet with this tool.

Input Schema

Name	Required	Description	Default
`network`	No		solana
`include_terminal`	No	True also returns completed and cancelled schedules. Default: live only.

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 2, 2026

TDQS

A4.8/5.0

Behavior5/5

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

Beyond the read-only nature implied by annotations, the description clarifies operational behavior by stating the default scope (live schedules only) and how to override it using `include_terminal`. It also asserts data privacy boundaries regarding the 'CURRENT USER's own data.'

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

While dense, the structure is highly logical, starting with a definition, moving to differentiation, and ending with operational rules and scope constraints. Every piece of information serves a critical function in guiding tool selection.

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 complexity and the necessity of distinguishing this tool from related siblings, the definition is highly complete. It covers functionality, scope, error handling, and usage context thoroughly.

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

Parameters4/5

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

The description adds semantic meaning to the `include_terminal` parameter by explaining that setting it returns 'finished and cancelled schedules,' adding necessary context beyond the schema's simple type definition. It also clarifies the scope of the `network` parameter contextually.

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 provides an explicit definition of DCA schedules (recurring buys/sells with slices and progress) and strongly differentiates the tool from the sibling `get_my_orders`, specifying it is for DCA rows alone.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

It provides explicit usage guidelines, instructing agents to use this tool when `get_my_orders` is insufficient, and offers a crucial workflow tip regarding the `dca_schedules_error` case, requiring a call to this tool before concluding there are no DCA schedules.

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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