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Band traceback (where the extra band came from)

band_traceback
Read-onlyIdempotent

Explain a band you measured on a gel. Given the template, both primers and the observed size, it enumerates every pair of priming sites — including a single primer priming both strands — that would give a product that size, and ranks them by how much of each primer's 3' end matches without interruption, which is what decides whether a mispriming event can extend at all. Reports no yield and assigns no share of the band: the band is the input, not the output. Says plainly when nothing on this template explains the size, and what that points to instead.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
circularNoTemplate is circular (a plasmid).
templateYesThe template you amplified, raw or FASTA. Max 200,000 bp.
toleranceBpNoOverride the tolerance with a fixed window in bp.
observedSizeYesThe band size you measured off the gel, in bp.
forwardPrimerYesForward primer, 5'→3'.
maxCandidatesNoCap on returned candidates, best-anchored first.
maxMismatchesNoMismatches tolerated per primer while looking for sites (max 6). Higher finds more explanations and more coincidences.
reversePrimerYesReverse primer, 5'→3'.
tolerancePercentNoHow far off the size read may be, as a percentage. A percentage rather than a fixed number of bases because ±10% of 200 bp is not the same reading precision as ±10% of 5 kb.

TDQS

A4.5/5.0
Behavior5/5

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

The description explicitly states what the tool does not do ('Reports no yield and assigns no share of the band') and explains its ranking logic based on 3' end matching. It also discloses failure behavior ('Says plainly when nothing on this template explains the size'). Annotations include readOnlyHint and idempotentHint, and the description aligns with these without contradiction.

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 appropriately sized—three sentences that front-load the purpose, then detail algorithm and expectations. Every sentence adds value, with no filler or repetition of schema details.

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 (algorithmic enumeration, ranking, and failure modes), the description covers what the tool does, what it doesn't do, and what it returns. There is no output schema, but the description sufficiently sets expectations for outputs and edge cases.

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?

The input schema already provides 100% description coverage for all 9 parameters. The description adds no extra meaning about individual parameters beyond restating that the tool uses template, primers, and observed size. It doesn't clarify format, units, or relationships beyond what the schema says, so it meets the baseline but doesn't exceed it.

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: to explain a band on a gel by enumerating priming-site pairs and ranking them. It uses a specific verb+resource ('Explain a band you measured') and distinguishes itself from siblings like in_silico_pcr by focusing on explaining observed bands rather than predicting them.

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 gives clear context (use when you have a measured band and want its origin) but does not explicitly name alternatives or state when not to use it. It does imply that it's for troubleshooting unexpected bands, but lacks direct comparisons to related tools like primer_specificity.

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.

Resources