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validate_incident

Submit validation results for incident reports to confirm or reject them before triggering emergency response actions. Used by human operators and automated systems to verify incident authenticity.

Instructions

Submit validation result for an incident report.

Used by human operators or automated validation systems (HCE) to confirm or reject incident reports before triggering full response.

Args: val: IncidentValidation with: - incident_id: ID of incident being validated - is_confirmed: True=confirmed, False=rejected/false positive - validation_source: Who/what validated (e.g., "Human-Operator") - correlated_pre_alert_id: Optional linked PDIE alert - notes: Validation reasoning and evidence

Returns: str: Confirmation message indicating action taken: "Incident {id} successfully CONFIRMED." or "Incident {id} successfully REJECTED."

Example: >>> from resq_mcp.models import IncidentValidation >>> validation = IncidentValidation( ... incident_id="INC-123", ... is_confirmed=True, ... validation_source="Human-Operator-Alice", ... notes="Confirmed via video evidence and ground reports" ... ) >>> result = await validate_incident(validation) >>> print(result) # "Incident INC-123 successfully CONFIRMED."

Workflow: 1. Edge AI detects incident (low confidence) 2. HCE cross-references with PDIE/sensors 3. If ambiguous → human review required 4. Operator submits validation via this tool 5. If confirmed → trigger response strategy 6. If rejected → log as false positive, update ML model

Audit Trail: All validations logged with timestamp, source, and reasoning for post-incident analysis and ML model refinement.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
valYesValidation result after cross-referencing an incident report. Part of HCE system. Produced after comparing incident reports against PDIE predictions, sensor networks, and historical data to confirm authenticity and trigger appropriate response protocols. Attributes: incident_id: ID of the incident being validated. is_confirmed: Whether the incident is confirmed as genuine. validation_source: System or agent that performed validation (e.g., "SpoonOS-HCE-Validator", "Human-Operator"). correlated_pre_alert_id: Related PDIE pre-alert if correlation found. notes: Detailed validation reasoning and cross-reference results. Example: >>> validation = IncidentValidation( ... incident_id="INC-123", ... is_confirmed=True, ... validation_source="SpoonOS-HCE-Validator", ... notes="Confirmed via PDIE correlation and sensor data" ... )

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it submits validation results, returns confirmation messages, logs all validations for audit trails, and triggers downstream actions (response strategy or false positive logging). However, it doesn't explicitly mention potential side effects like rate limits, authentication requirements, or error conditions, though the workflow implies some system integration.

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

Conciseness4/5

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

The description is well-structured with clear sections (description, args, returns, example, workflow, audit trail) and front-loaded key information. While comprehensive, it includes some redundant details (e.g., the example partially repeats schema information) that could be trimmed. Every sentence contributes to understanding, but it's slightly verbose compared to ideal conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (validation with workflow implications), no annotations, rich input schema (100% coverage), and presence of an output schema, the description is complete. It covers purpose, usage context, parameter semantics, return values, examples, workflow integration, and audit logging. The output schema handles return value documentation, so the description appropriately focuses on operational context without duplicating structured data.

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 100%, so the schema already documents the single parameter 'val' and its nested properties thoroughly. The description adds value by explaining the parameter's role ('Validation result after cross-referencing an incident report') and providing a concrete example with context. However, it doesn't add significant semantic information beyond what's in the schema descriptions, keeping it at a strong but not exceptional level.

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: 'Submit validation result for an incident report' with specific verbs ('submit validation result') and resources ('incident report'). It distinguishes from siblings (get_deployment_strategy, run_simulation) by focusing on validation rather than retrieval or simulation. The description elaborates on the validation context, 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 Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool: 'Used by human operators or automated validation systems (HCE) to confirm or reject incident reports before triggering full response.' It provides a detailed workflow (steps 1-6) showing the tool's role in the incident validation process, including prerequisites (e.g., 'If ambiguous → human review required') and alternatives (e.g., 'If rejected → log as false positive'). This gives clear context for when this tool should be invoked versus other actions.

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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