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alopez3006

snipara-mcp

by alopez3006

rlm_htask_create

Create hierarchical tasks at any level (N0-N3) with owners, priorities, acceptance criteria, and evidence requirements.

Instructions

Create a hierarchical task at any level (N0-N3).

Supports 4-level hierarchy: N0_INITIATIVE > N1_FEATURE > N2_WORKSTREAM > N3_TASK. Tasks have owners, priorities, acceptance criteria, and evidence requirements.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
swarm_idYesSwarm ID
levelNoTask hierarchy levelN3_TASK
titleYesTask title
descriptionYesTask description
ownerYesTask owner (required)
parent_idNoParent task ID (required for N1-N3)
priorityNoPriority levelP1
eta_targetNoTarget completion date (ISO format)
execution_targetNoWhere the task executes
workstream_typeNoWorkstream type for N2 tasks
acceptance_criteriaNoList of acceptance criteria [{id, text, checked}]
context_refsNoContext references (URLs, file paths)
context_queryNoAuto-fetch relevant docs via rlm_context_query and add to context_refs (e.g., 'JWT authentication patterns')
evidence_requiredNoRequired evidence [{type, description}]
is_blockingNoWhether task blocks parent closure when failed/incomplete
Behavior3/5

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

No annotations are provided, so the description carries full burden. It lists supported fields (owners, priorities, etc.) but does not disclose behavioral traits like side effects, return values, error conditions, or constraints. For a creation tool, the transactional nature is implied but not explicitly confirmed.

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, front-loaded with the core action. Every sentence adds value (purpose and key features), no redundancy. Ideal conciseness for a tool with rich schema.

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

Completeness3/5

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

Given 15 parameters and no output schema, the description is too brief to fully guide usage. It covers hierarchy and key fields, but lacks guidance on complex parameters (acceptance_criteria, context_query) and return value expectations. Schema descriptions help, but the description could be more complete.

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%, so baseline is 3. The description adds minimal value beyond the schema—it repeats the hierarchy structure and field categories but does not explain how parameters interact (e.g., parent_id requirements per level) or provide usage examples.

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 verb 'create', the resource 'hierarchical task', and the scope 'at any level (N0-N3)'. It distinguishes from siblings like rlm_task_create by emphasizing the hierarchy, making 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 Guidelines3/5

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

The description implies usage for hierarchical tasks but does not explicitly state when to use it over alternatives (e.g., rlm_task_create, rlm_htask_create_feature). No when-not or exclusion criteria are provided, leaving ambiguity.

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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