gene_expressionA gene's tissue-expression fingerprint: per-tissue median TPM from GTEx (v8) and subcellular localization / RNA tissue-specificity / protein class from the Human Protein Atlas, in one call.
| 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 indicate readOnlyHint=true and idempotentHint=true, so the tool is safe and idempotent. The description adds value by detailing the specific data sources (GTEx v8, Human Protein Atlas) and the types of data returned, providing useful context beyond what annotations alone convey. However, it does not mention rate limits or other constraints.
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 front-loads the key concept ('tissue-expression fingerprint'). It includes necessary detail without verbosity, though it could be slightly more concise by breaking the list of data types into separate points.
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 the complexity of returning multiple data types from two databases, the description adequately lists what is included. However, it does not describe the output format or how to interpret the fingerprint, leaving some gaps for an agent without an output schema.
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 sole parameter 'gene' is described in the schema with examples of symbol or Ensembl ID. The tool description does not add additional meaning beyond the schema, as it only repeats the concept of a gene. Schema description coverage is 100%, so baseline 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 the tool returns a 'gene's tissue-expression fingerprint' and lists specific data types (per-tissue median TPM, subcellular localization, RNA tissue-specificity, protein class) from GTEx and Human Protein Atlas. This distinctively identifies the resource and operation, differentiating it from sibling tools that cover sequence analysis, CRISPR design, etc.
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 implies use when needing gene expression data but does not explicitly state when to use this tool versus alternatives or when not to use it. No exclusions or alternative tool names are provided, leaving the agent to infer the context from sibling names alone.
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.