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ALTR MCP Server

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create_report_definition

Create a report definition to configure audit data filters, schedule, and delivery. After creation, trigger the report on demand.

Instructions

Create a new audit report definition.

Defines what data is included, how it is scheduled, and where it is delivered. After creating, use trigger_report_definition to generate a report on demand.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesUnique display name for the definition.
integration_typeYesData source type. Values: "oltp", "snowflake".
descriptionNoOptional human-readable description.
lookback_daysNoNumber of complete calendar days to include in each report window (excludes the trigger day).
timezoneNoIANA timezone for the report window (e.g. "America/New_York").
schedule_cronNo6-field cron expression controlling when the report runs automatically. Format: "minute hour day-of-month month day-of-week year" Use ? in day-of-month OR day-of-week (not both) when the other field is specified. Use * for "every". Days: SUN MON TUE WED THU FRI SAT Months: JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC Common examples — convert natural language like: "every day at 12 PM" → "0 12 * * ? *" "every day at 9 AM" → "0 9 * * ? *" "every Monday at 9 AM" → "0 9 ? * MON *" "every weekday at 8:30 AM" → "30 8 ? * MON-FRI *" "every Sunday at 6 PM" → "0 18 ? * SUN *" "first day of month midnight" → "0 0 1 * ? *" "every hour" → "0 * * * ? *"
schedule_enabledNoWhether the schedule is active.
schedule_timezoneNoIANA timezone for schedule evaluation (e.g. "America/New_York"). All cron times are interpreted in this timezone.
deliveryNoDelivery configuration as a dict or JSON string. Shape: {"channels": [{"type": "email", "enabled": bool, "recipients": ["email@example.com"]}]}.
filtersNoFilter groups as a dict or JSON string. Shape: {"filter_groups": [{"filters": [{"field": "database_name", "pattern": {"match_type": "exact", "value": "mydb"}}]}]}. OLTP fields: database_name, table_name, schema_name, column_name, statement_type, consuming_user, event_source, event_name, repo_user, repo_host, repo_name, repo_type, application_name, client_host, connection_id, statement_text, policy_blocked, execution_success, row_count. Snowflake fields: username, current_role, ip_address, client, query_type, warehouse, warehouse_size.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

With no annotations provided, the description bears full responsibility for disclosing behavioral traits. It states it creates a new definition but does not mention whether it is idempotent, what happens if a definition with the same name exists, authentication requirements, or any side effects. This lack of detail leaves the agent uninformed about important behavioral boundaries.

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 extremely concise: two sentences plus a header. The first sentence states the primary purpose, and the second adds immediate next-step guidance. Every word earns its place with zero redundancy.

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's complexity (10 parameters) and the presence of an output schema (not shown), the description covers the main creation intent and workflow linkage. It could mention that the tool returns the created definition object, but since output schema exists, it is not strictly required. Overall, it provides sufficient context for an agent to understand and correctly invoke the tool.

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 input schema has 100% description coverage, so the baseline is 3. The description adds value by providing a high-level overview of what the parameters collectively achieve and includes extensive, practical examples for the 'schedule_cron' parameter (e.g., common cron patterns) and clear shapes for 'delivery' and 'filters'. This goes beyond the schema's individual descriptions.

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 action ('Create') and the resource ('new audit report definition'), with a verb+resource structure. It also distinguishes from sibling tool 'trigger_report_definition' by mentioning the explicit next step, making the purpose unambiguous.

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 provides clear guidance on the typical workflow by stating 'After creating, use trigger_report_definition to generate a report on demand.' This implies when to use this tool and suggests an alternative for report generation. However, it does not explicitly mention when not to use it or contrast with other creation tools in the sibling list.

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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