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Oligo pool cross-dimer screen

oligo_pool_screen
Read-onlyIdempotent

Screen a whole set of oligos you already have — every pair for cross-dimers, every oligo for its own hairpin and self-dimer, and the set for duplicates and Tm spread — and get back the conflicts ranked rather than a table of every combination. This is the pool-level answer cross_dimer gives one pair at a time: 51 primers is 1,275 pairs, which is 1,275 separate calls done by hand and one call done here. Not to be confused with multiplex_panel_design, which DESIGNS primers from templates; this takes the primers you have already ordered. A pairing that involves an oligo's 3' END is judged at a weaker ΔG than one that only pairs internally, because that end is where extension starts — the same two-bar rule the multiplex panel designer uses. Every number is a nearest-neighbour calculation over the sequences supplied, not a prediction of what the reaction will do.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
mgMMNoDivalent cation [Mg2+] (mM).
naMMNoMonovalent cation [Na+]/[K+] (mM).
fastaNoThe pool as a FASTA paste (headers become the names), or as one sequence per line — which is what comes out of a spreadsheet column. An alternative to `oligos`.
dntpMMNoTotal [dNTP] (mM), chelates Mg2+.
oligosNoThe pool, as objects with a name and a sequence. Up to 384. Use this or `fasta`, not both.
oligoNMNoTotal strand concentration (nM).
maxConflictsNoConflict rows returned, most stable first. The COUNTS are always complete.
internalThresholdDGNoThe looser bar for a pairing that only sequesters primer internally, away from either 3' end.
threePrimeThresholdDGNoΔG (kcal/mol) at or below which a pairing that involves a 3' END counts as a conflict.

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark the tool read-only and idempotent; the description adds the output style (conflicts ranked, complete counts), the two-bar 3' vs internal ΔG rule, and the caveat that values are nearest-neighbour calculations, not reaction predictions. This is useful behavioral context beyond the annotations and no contradiction is present.

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 five sentences and all of them carry information: purpose, pool-level comparison with a concrete scale example, differentiation from the design tool, the scoring rule, and the calculation caveat. The 51-primer example is illustrative but slightly expandable; still, nothing is redundant.

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

Completeness4/5

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

For a read-only screening tool with rich annotations and a fully documented schema, the description adequately covers what the tool does, its relationship to close siblings, and its output style. With no output schema, the 'conflicts ranked' and 'COUNTS always complete' statements help fill that gap.

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?

With 100% schema description coverage, every parameter is already documented in the schema. The description adds conceptual context for the two ΔG thresholds, but no additional per-parameter semantics beyond what the schema provides, so a baseline 3 is appropriate.

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 uses a specific verb ('Screen') and a defined resource ('a whole set of oligos you already have'), enumerates the checks performed (cross-dimers, hairpins, self-dimers, duplicates, Tm spread), and explicitly contrasts with cross_dimer and multiplex_panel_design, making sibling differentiation clear.

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?

It explicitly frames this tool as the pool-level counterpart to cross_dimer, notes the scale difference through a concrete example, and warns against confusing it with multiplex_panel_design by specifying that the tool consumes already-ordered oligos rather than designing them. This gives an agent clear selection criteria.

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