codon_adaptation_indexCodon Adaptation Index (CAI) and per-codon relative adaptiveness of a CDS against an expression host, with rare-codon and GC3 analysis.
| Name | Required | Description | Default |
|---|---|---|---|
| organism | No | ecoli | |
| sequence | Yes | Coding sequence (DNA/RNA; should start in-frame at ATG). | |
| frameStart | No | 1-based position to start reading codons. | |
| rareThreshold | No | Relative adaptiveness (w) below this flags a codon as rare. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and idempotentHint=true, so the description does not need to restate these. The description adds context about the analysis outputs (CAI, adaptiveness, rare-codon, GC3) but does not disclose any unexpected behaviors, side effects, or limitations beyond what annotations provide.
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?
The description is a single well-formed sentence that efficiently conveys the tool's purpose without redundancy. Every word contributes to meaning, making it easy to scan and understand quickly.
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?
The tool has moderate complexity (4 parameters, no output schema), but the description does not explain what the tool returns (e.g., numerical values, plots, or a report). It omits details about output format, error conditions, or required input validation, leaving the agent to infer behavior.
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 description coverage is 75%, and the schema itself provides clear descriptions for all parameters (e.g., sequence: 'should start in-frame at ATG'). The description adds no new meaning beyond listing the analysis components, so it meets the baseline but does not enhance parameter understanding.
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?
The description explicitly states that the tool computes Codon Adaptation Index and per-codon relative adaptiveness for a coding sequence against an expression host, including rare-codon and GC3 analysis. This clearly identifies the tool's specific function and distinguishes it from sibling tools like codon_optimize and gc_content.
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?
The description provides no guidance on when to use this tool versus alternatives (e.g., codon_optimize for modification, gc_content for simple GC). It does not mention exclusions or prerequisites, such as requiring a valid coding sequence starting with ATG.
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.