oligo_analysisFull oligo analysis: nearest-neighbour Tm/ΔG/ΔH/ΔS plus hairpin and self-dimer screening with base-pair diagrams and warnings.
| Name | Required | Description | Default |
|---|---|---|---|
| mgMM | No | Divalent cation [Mg2+] (mM). | |
| naMM | No | Monovalent cation [Na+]/[K+] (mM). | |
| dntpMM | No | Total [dNTP] (mM), chelates Mg2+. | |
| oligoNM | No | Total strand concentration (nM). | |
| sequence | Yes | Nucleotide sequence (raw or FASTA; IUPAC accepted). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint=true and idempotentHint=true, so safety is clear. Description adds that it returns base-pair diagrams and warnings, which is useful but does not elaborate on computational cost or limitations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence that is front-loaded with key outputs (Tm, ΔG, ΔH, ΔS, screening, diagrams, warnings). No unnecessary words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema provided, so description must explain return values. It lists thermodynamics, screening results, and diagrams, which is sufficient but lacks detail on format or structure (e.g., whether it returns a report, JSON, or visualization).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers 100% of parameters with descriptions (e.g., mgMM, naMM, dntpMM, oligoNM, sequence). The description only restates that it uses sequence and cation concentrations, adding no new meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states the tool performs full oligo analysis including nearest-neighbour thermodynamics (Tm, ΔG, ΔH, ΔS) and secondary structure screening (hairpin, self-dimer) with diagrams and warnings. This distinguishes it from siblings like 'melting_temperature' which only calculates Tm.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives (e.g., 'melting_temperature' for just Tm, 'cross_dimer' for heterodimers). The description implies comprehensive analysis but does not state use cases or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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'.
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.
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.