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NGS360

NGS360 MCP Server

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by NGS360

create_qcrecord

Creates a quality control (QC) record for a project or sequencing run, capturing metrics and output files to track data quality.

Instructions

Create a new QC record with metrics and output files.

Exactly one of project_id or sequencing_run_id must be provided.

Args: project_id: Project-scoped record (e.g., P-1234) sequencing_run_id: Run-scoped record (run_id string) workflow_run_id: Optional workflow run UUID (provenance) metadata: Pipeline metadata {"pipeline": "RNA-Seq", "version": "2.0"} metrics: List of metric groups, each with: - name: Metric group name - samples: Optional [{"sample_name": "S1", "role": "tumor"}] - values: {"reads": 50000000, "alignment_rate": 95.5} output_files: Optional list of file records to create

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
metricsNo
metadataNo
project_idNo
output_filesNo
workflow_run_idNo
sequencing_run_idNo
Behavior2/5

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

With no annotations, the description carries full burden for behavioral disclosure. It only states 'Create' (implying mutation) and lists parameters, but does not describe permissions, side effects, idempotency, or what happens on failure. This is insufficient for a write operation.

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 relatively concise, with a short introductory sentence and a bullet-style Args list. It is well-structured and front-loaded with the key constraint. Minor excess: the Args list could be slightly more compact, but overall efficient.

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 the tool's complexity (6 parameters, no output schema, no annotations), the description covers parameter usage well but lacks behavioral context (e.g., return value, success/failure behavior). Without an output schema, some mention of what is returned would improve completeness.

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 input schema has 0% parameter description coverage, so the description must compensate. It provides detailed explanations for each parameter, including examples (e.g., project_id: 'P-1234', metadata example, metrics structure). This adds significant meaning beyond the bare 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 'Create a new QC record with metrics and output files', providing a specific verb and resource. It distinguishes from sibling tools like delete_qcrecord and get_qcrecord, which are different operations.

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 explicitly states that exactly one of project_id or sequencing_run_id must be provided, which is a critical usage constraint. However, it does not mention when to use this tool versus other creation tools (e.g., create_run, create_file_record) or when not to use it, missing some guidance.

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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