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verify_email

Complete clariBI signup by submitting the verification code plus a password. Returns an OAuth access_token for immediate use AND a long-lived MCP API key for persistent configuration. The new organization lands on the Trial tier (50 AI credits, 14 days).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYes6-digit verification code from the email.
passwordYesA password for the new account. Must be at least 8 characters and pass Django's standard validators.
pending_idYesThe pending_id returned by register_account.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
tierYesNew organization tier, always "trial".
scopeYesSpace-separated granted scopes.
user_idYes
expires_inYesAccess-token lifetime in seconds.
next_stepsNo
token_typeYesAlways "Bearer".
mcp_api_keyYesLong-lived API key (claribi_mcp_...) for persistent client config.
access_tokenYesOAuth bearer token for this conversation.
mcp_api_key_idYes
organization_idYes

TDQS

A4.2/5.0
Behavior4/5

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

Annotations provide minimal signal (all false). The description adds valuable behavior: it returns an OAuth access_token and MCP API key, and places the org on Trial tier (50 credits, 14 days). It does not detail side effects like single-use codes, but the key behavioral outcomes are disclosed without contradicting annotations.

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 two sentences, efficiently front-loaded with the core action and followed by return value and account tier. Every sentence adds value with no redundancy or filler.

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?

The description covers the tool's purpose, return value, and post-condition (Trial tier), and an output schema exists to detail the return structure. It does not explicitly state the dependency on register_account, but the schema's pending_id reference and sibling context make it adequately complete for a signup completion tool.

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% for all parameters, so the baseline is 3. The description adds no extra parameter semantics beyond mentioning 'verification code plus a password' and does not reference pending_id, but the schema fully documents each field with constraints and origins.

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 the specific action: 'Complete clariBI signup by submitting the verification code plus a password.' This uses a precise verb ('complete') and resource ('clariBI signup'), distinguishing it from siblings like register_account (which initiates signup) and other unrelated tools.

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 description implies usage context: it is the signup completion step, referencing the pending_id from register_account in the schema. It clearly indicates when to use, but does not explicitly state exclusions or alternatives. However, because it is uniquely paired with register_account, the guidance is sufficient.

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

A3.9/5.0
Disambiguation4/5

Tools are generally distinct by resource and action, but a few status polling tools (check_integration_status, get_analysis_status) could be confused without careful reading; descriptions clarify the difference.

Naming Consistency4/5

Most tools use a consistent verb_noun snake_case pattern, but there is minor variation (e.g., 'check' vs 'get' for status, and some compound nouns like 'request_oauth_integration_url').

Tool Count4/5

26 tools is slightly above the typical range but appropriate for a comprehensive BI platform covering data ingestion, analysis, forecasting, reports, dashboards, and account management; each tool has a clear purpose.

Completeness2/5

The tool surface is heavily read-oriented, lacking update and delete operations for most resources (data sources, dashboards, reports, forecasts). This leaves significant lifecycle management gaps for an agent.

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