workbench

FASTQ Deep QC Report

fastq_qc_report

Read-onlyIdempotent

FastQC-style deep quality-control report for a FASTQ file: per-base quality and content, GC and length distributions, sequence duplication levels, overrepresented sequences, and adapter content — each with a warn/fail verdict against FastQC's own published thresholds.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`input`	Yes	FASTQ text: records of an '@id' header, sequence, '+' separator and quality line (four lines each).
`qualityOffset`	No	FASTQ Phred ASCII offset (33 = Sanger/Illumina 1.8+, 64 = Illumina 1.3-1.7).

TDQS

A3.6/5.0

Behavior3/5

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

Annotations already provide readOnlyHint=true and idempotentHint=true, so the tool's safety is clear. The description adds detail on the report contents (e.g., per-base quality, GC content) but does not reveal additional behavioral traits like output format or potential limitations.

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 a single, well-structured sentence that front-loads key information about the tool's purpose and components. No redundant words.

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

Completeness2/5

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

Despite no output schema, the description does not specify the output format (JSON, text, etc.) or provide details on return structure. For a complex tool generating multiple QC modules, this omission leaves the agent uncertain about what to expect.

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?

Input schema has 2 parameters with 100% description coverage, so the schema already documents them. The description adds no extra meaning beyond the schema, meeting the baseline of 3.

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 identifies the tool as a FastQC-style deep QC report for FASTQ files, listing specific modules (per-base quality, GC distribution, etc.) and mention of warn/fail verdicts against FastQC thresholds. This distinguishes it from siblings like fastq_trim or seqfile_stats.

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 does not explicitly state when to use this tool versus alternatives like seqfile_stats for basic stats or fastq_trim for trimming. Usage context is implied by the tool's purpose, but no explicit when-to-use or when-not-to-use guidance is provided.

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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TDQS

A3.6/5.0

Disambiguation4/5

Most tools have highly specific purposes (e.g., crispr_grna_design vs base_editing_design vs prime_editing_design). However, there is some overlap in sequence analysis tools (characterize_sequence, sequence_report) and plasmid annotation tools (plasmid_annotate vs plasmid_deep_annotate) which could cause confusion.

Naming Consistency3/5

The naming pattern is largely consistent with snake_case verb_noun or noun_descriptor (e.g., primer_design, plasmid_annotate, fastq_trim). However, there are exceptions like 'batch', 'workflow', 'gc_content', and 'cloning_diagnose' which don't follow the verb_noun pattern consistently. Also, some names are phrases like 'golden_gate_from_parts'.

Tool Count2/5

With 101 tools, this server is extremely large and likely overwhelming for agents. Even for a comprehensive bioinformatics toolkit, this exceeds a manageable scope, risking agent confusion and inefficient tool selection. A more modular approach would be advisable.

Completeness4/5

The tool surface covers a wide range of bioinformatics workflows including sequence analysis, primer design, cloning, CRISPR, NGS, expression analysis, and data export. There are minor gaps such as lack of a dedicated protein structure prediction tool and limited off-target genome coverage, but overall the set is impressively complete for its domain.

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