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latent-defense

Latent Defense MCP Server

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create_connector

Creates a data source connector to automatically ingest artifacts from security tools like AWS GuardDuty, Qualys, or Tenable.

Instructions

Create a data source connector for automated artifact ingestion.

Args: name: Connector display name. connector_type: Type (use list_connector_types to see available). E.g. "aws_guardduty", "aws_inspector", "qualys", "tenable". connection_config: JSON object with type-specific connection params (credentials, regions, etc.). poll_config: Optional JSON object with polling settings (interval_minutes, enabled, etc.). mapping_config: Optional JSON object with field mapping overrides.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
connector_typeYes
connection_configYes
poll_configNo{}
mapping_configNo{}

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

With no annotations provided, the description must disclose behavioral traits. It states the tool creates a connector but does not mention side effects (e.g., whether it triggers ingestion, required permissions, or account limits). This omission is significant for a mutation tool.

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 concise: a single-line purpose followed by a clear Args block. Every sentence adds value with no redundancy. It is well-structured and front-loaded.

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 adequately explains all parameters, and the presence of an output schema covers return values. However, it lacks behavioral context and usage guidelines, which are partially offset by the sibling tools' existence. Overall, it is sufficiently complete for a create operation.

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

Parameters5/5

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

The schema provides only titles and types (0% description coverage), so the description fully compensates by explaining each parameter's purpose, format (e.g., JSON object), examples for connector_type, and optionality. This adds critical meaning beyond the schema.

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 tool's purpose: 'Create a data source connector for automated artifact ingestion.' It uses a specific verb ('create') and resource ('data source connector'), and distinguishes from sibling tools like list_connector_types and update_connector.

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 advises using list_connector_types to see available connector types, providing a helpful usage hint. However, it lacks explicit guidance on when to use this tool versus alternatives like update_connector or delete_connector.

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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