gene_modelThe real exon/UTR/CDS structure of a human gene's canonical transcript, fetched live from Ensembl (the same exon/CDS map the HGVS Converter tool uses) — for rendering an exon diagram.
| Name | Required | Description | Default |
|---|---|---|---|
| gene | Yes | A human gene symbol ("TP53") or Ensembl gene ID ("ENSG00000141510"). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already convey readOnlyHint=true and idempotentHint=true. The description adds valuable context: the data is fetched live from Ensembl, uses the canonical transcript, and mirrors the HGVS Converter tool's map. No contradictions, and the extra details help the agent understand behavior beyond annotations.
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, efficient sentence that conveys the core function and purpose without superfluous words. Every element earns its place.
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 low complexity and good annotations, the description adequately explains what the tool does and its source. However, without an output schema, it lacks specifics about the returned data structure (e.g., coordinate lists), which could limit an agent's ability to use the output effectively.
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?
The only parameter 'gene' is fully described in the schema (100% coverage). The tool description adds no additional meaning beyond the schema, so a baseline score of 3 is appropriate.
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 states it fetches the exon/UTR/CDS structure from Ensembl for a human gene's canonical transcript, with a specific purpose of rendering an exon diagram. It distinguishes from siblings by specifying the source (Ensembl) and transcript selection (canonical), but could more explicitly contrast with similar tools like gene_dossier.
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 lacks explicit guidance on when to use this tool versus alternatives. It mentions a use case (rendering an exon diagram) but does not provide exclusions, prerequisites, or compare with other tools from the sibling list.
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.