protein_propertiesProtein properties: molecular weight, isoelectric point, GRAVY, extinction coefficient and composition.
| Name | Required | Description | Default |
|---|---|---|---|
| sequence | Yes | Protein sequence (one-letter amino-acid codes; non-AA characters ignored). | |
| chargeStep | No | pH step along the net-charge titration curve, which always spans pH 0-14. Between 0.001 and 14. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint and idempotentHint, so no contradiction exists. The description adds only a little context by listing the returned property kinds, but it does not clarify behavioral details such as the net-charge/titration output associated with chargeStep or how the computation handles invalid input beyond what the schema already states.
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 front-loaded line with no filler. Every word contributes to enumerating the tool's outputs, and it is appropriately sized for a simple read-only property calculator.
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?
Given two well-documented parameters, read-only/idempotent annotations, and a straightforward computation, the description is nearly sufficient. It would be more complete if it explicitly connected chargeStep to a net-charge output, but the schema already provides that context, so the gap is minor.
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 100%: the schema already documents sequence format and chargeStep constraints in detail. The tool description adds no parameter-specific meaning, so it stays at the baseline 3 rather than compensating or improving on 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?
The description clearly enumerates the computed properties—molecular weight, isoelectric point, GRAVY, extinction coefficient, and composition—so an agent can tell this is a protein-sequence analysis tool. However, it lacks a specific verb like 'compute' or 'predict' and does not explicitly differentiate it from siblings such as protein_hydrophobicity or characterize_sequence.
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?
There is no guidance about when to use this tool versus the many related sequence-analysis siblings, nor any statement about cases where it should not be used. The list of properties implies a use case, but the description leaves selection entirely to inference.
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.