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Zenskar MCP Server

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ingestRawMetricEvent

ingestRawMetricEvent

Ingest a usage event payload into a specified raw metric schema to record customer usage data, using the resource slug to identify the metric.

Instructions

Ingest a Usage Event for the specified resource slug. Host enforces user confirmation via the approval gate; do NOT ask the user to re-confirm before calling.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rawMetricSlugYesSlug of the Usage Event resource (raw metric), e.g. api_usage_events.
eventNoUsage Event payload containing customer_id, timestamp, and a data map with schema-compliant types. customer_id and timestamp are mandatory event fields and must be provided in the payload; MCP does not auto-add them. Before constructing the event, fetch the schema via getRawMetricBySlug (or reuse the response from createRawMetric) so you only include fields that exist in the Usage Event schema (raw metric schema). Use the provided type buckets such as String/Int64 or the exact column names returned by sample logs; do not invent new keys.
__userContextNoInternal user context for multi-tenant authentication and approval workflow
Behavior4/5

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

The description discloses that host enforces user confirmation via an approval gate. With no annotations, this is valuable behavioral context. The description does not mention other aspects like side effects or error responses, but the key behavior is covered.

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 tool description is two sentences long, front-loading the purpose and key behavioral instruction. Every sentence adds value; no 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?

For a mutation tool with no output schema, the description covers the core purpose, a critical behavioral trait (approval gate), and detailed parameter guidance. Missing return value details are a minor gap, but overall it equips an agent adequately.

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 coverage is 100%, so baseline is 3. The description of the 'event' parameter adds significant meaning: it explains the mandatory fields and instructs to fetch the schema first. This goes beyond the schema alone.

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?

Description clearly states the tool's verb ('Ingest') and resource ('Usage Event for the specified resource slug'). Among siblings, no other tool has 'ingest' in its name, so purpose is distinct and 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?

Description explicitly instructs not to ask the user for confirmation because the host's approval gate handles it. This is a concrete usage guideline. However, it does not discuss when to use this tool versus alternatives or prerequisites beyond the event construction.

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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