Retrieve OncoKB annotations for a cancer gene variant using gene symbol and protein change to support variant interpretation and clinical evidence lookup.
Get OncoKB annotations for a variant in a cancer gene. Requires ONCOKB_TOKEN environment variable.
| Name | Required | Description | Default |
|---|---|---|---|
| gene | Yes | Gene symbol (e.g., BRAF, EGFR) | |
| protein_change | Yes | Protein change (e.g., V600E, L858R) |
Changes observed during successful MCP inspections.
v1.6.0-alpha.10Input schema / additionalPropertiesRemoved value: -falsev1.6.0-alpha.7Input schema / properties / gene / minLengthAdded value: +1Input schema / properties / protein_change / minLengthAdded value: +1v0.1.8Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and openWorldHint=true, so safety and external-call behavior are covered. The description adds a genuinely useful operational prerequisite not present in structured fields – the ONCOKB_TOKEN environment variable – but says nothing about behavior when the token is missing, rate limits, or partial/absent annotations for a variant.
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?
Two short sentences, front-loaded with the purpose and followed by the prerequisite; nothing is padded. It is perhaps overly terse given the tool's domain complexity, but no sentence is wasted.
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?
Inputs are fully covered by the schema and there is no output schema to explain, but the description gives no hint of what OncoKB annotations contain (e.g., oncogenicity, evidence level, therapies), which matters for an agent deciding whether this tool answers its question. Adequate but with a clear gap given the specialized data source.
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%, with both 'gene' and 'protein_change' documented with examples (BRAF, V600E), so the schema carries the load. The description only restates the entities at a high level and adds no format or validation semantics beyond it – baseline 3 applies.
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?
Specific verb+resource: 'Get OncoKB annotations for a variant in a cancer gene' clearly identifies the data source (OncoKB) and the input entity (variant), distinguishing it from generic siblings like variant_get or variant_search. It does not explicitly name the sibling it differs from, so it falls short of a 5.
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 on when to prefer this over variant_get, variant_search, or gene_get, nor any exclusion conditions. The only contextual hint is the phrase 'in a cancer gene', which implies oncology relevance but leaves the choice between siblings to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.