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Submit onboarding survey

submit_onboarding
Idempotent

Store the onboarding survey (requires api:account). Current schema is version 4. q1 investing experience: 10y_plus | 5_10y | 2_5y | 1_2y | under_1y | not_started. q2 markets (one or more): fx | commodities | bonds | us_equities | non_us_equities | crypto | index_funds. q3 risk: no_risk | low | medium | high. q4 shape: conviction | diversified | no_preference. q5 and q6 are optional lists. q5: market_basics | market_briefs | stock_highlights | crypto_basics | innovation_digest. q6: substack_private | social | word_of_mouth | other. q3=no_risk stores the answer but does not provision an agent. After a successful submit, send the user to the agent_setup_url from get_setup_status so the app can derive the trading profile and they can approve the agent wallet.

Input Schema

NameRequiredDescriptionDefault
q1Yes
q2Yes
q3Yes
q4Yes
q5No
q6No

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare write (readOnlyHint=false), non-destructive, and idempotent. The description adds real value beyond them: the required api:account scope, the side-effect nuance that q3=no_risk stores the answer without provisioning an agent, and the post-submit handoff. This is meaningful behavioral context not present in the annotations.

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

Conciseness3/5

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

Front-loads purpose well and is logically ordered, but it re-enumerates every enum value that already appears verbatim in the input schema, which is wasted text. Only the parameter labels and behavioral notes earn their place.

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 6-param write tool with no output schema, the definition covers the auth scope, all parameter meanings, the conditional provisioning rule, and the required next action. The only gap is an explicit statement of what a successful submit returns, which is partially implied by the handoff instruction.

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?

Schema description coverage is 0%, so the description must compensate, and it labels every parameter (q1 investing experience, q2 markets, q3 risk, q4 shape, q5/q6 optional lists). The enum value re-listing is largely redundant with the schema, but the semantic labels and the q5/q6 optionality note genuinely add meaning the schema lacks.

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?

States a specific verb+resource ('Store the onboarding survey') and frames it with schema versioning ('Current schema is version 4'). No sibling duplicates this action, and the follow-up reference to get_setup_status makes the workflow boundary explicit.

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?

Gives clear context: submit the completed survey, then route the user to agent_setup_url from get_setup_status. It also names the conditional branch (q3=no_risk) that changes downstream behavior. No explicit when-not-to-use, but the workflow framing is strong.

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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