Cited gene, variant (rsID) and CPIC drug–gene lookups for AI agents. Read-only, no key.
Scored across 4 tools
list_genes and lookup_variant are clearly distinct, while lookup_gene and lookup_drug_gene have some overlap because both can surface drug interactions. However, lookup_gene is a comprehensive gene monograph and lookup_drug_gene is specifically pharmacogenomic guideline guidance, so agents can usually tell them apart.
All tool names follow a consistent lowercase verb_noun pattern: list_ for browsing collections and lookup_ for retrieving specific entities. The compound lookup_drug_gene is readable and fits the same convention, with no mixed naming styles.
Four tools is well-scoped for a read-only pharmacogenomics knowledge base: browse genes, get gene detail, get drug-gene guidance, and look up a variant. Each tool serves a distinct query path without unnecessary bloat or redundancy.
The toolset covers the main entry points for pharmacogenomic information: genes, variants, and gene-drug guidance. A minor gap is the lack of a drug-first lookup to find all relevant genes for a given drug, but the gene-centric design appears intentional and most user workflows are supported.
List the genes in the DeepDNA knowledge base, with a one-line summary and category for each.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the operation as read-only, idempotent, and non-destructive, covering the safety profile. The description adds behavioral context by revealing the output structure (one-line summary and category per gene), which is useful for the agent's expectation of the response. This goes beyond what annotations convey.
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 core action ('List the genes') and then adds the output detail. Every word earns its place, with no redundant or filler content, making it highly concise and well-structured.
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?
For a simple listing tool with no parameters and no output schema, the description fully covers what an agent needs to call it correctly: the action, the source (DeepDNA knowledge base), and the output content (summary and category). There are no significant gaps, and the sibling tools are clearly differentiated by their lookup nature.
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 tool has zero parameters, so the schema is empty and provides complete coverage (100%). There are no parameter details to explain, and the description correctly omits any. The baseline of 4 applies because there is nothing to compensate for.
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 lists genes from the DeepDNA knowledge base and specifies the output includes a one-line summary and category. This is a specific verb-resource pair that inherently distinguishes it from the sibling lookup tools, which focus on retrieving individual records rather than enumerating all entries.
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 does not explicitly state when to use this tool versus the sibling lookup tools. The intent is implied – use this for an overview of all genes, while lookups are for specific queries – but no explicit when/when-not guidance or named alternatives are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Pharmacogenomic guidance for a gene, optionally for one drug: guideline (CPIC/DPWG), summary of the recommendation, phenotypes and sources. Omit drug to list every drug curated for that gene.
| Name | Required | Description | Default |
|---|---|---|---|
| drug | No | Generic drug name, e.g. clopidogrel. Optional. | |
| gene | Yes | Pharmacogene symbol, e.g. CYP2C19. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. The description adds behavioral context: it indicates that omitting the drug parameter changes the result from a single drug-gene entry to a list of all curated drugs for that gene. It also names the types of data returned. This is meaningful 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?
Two sentences; the first states the core purpose and returned content, the second a concise conditional usage note. No filler; front-loaded and efficient.
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?
For a simple read-only lookup with two parameters and no output schema, the description covers what it returns, the optionality of drug, and the behavior when drug is omitted. It lacks explicit return format or error conditions, but annotations cover safety and the scope is small.
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 coverage is 100%, so the baseline is 3. The description adds semantics for the optional drug parameter by explaining the effect of omission ('Omit drug to list every drug curated for that gene'), which is not present in the schema's description ('Optional'). The gene parameter is not elaborated beyond 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 states a specific verb-resource: it returns pharmacogenomic guidance (CPIC/DPWG, recommendation, phenotypes, sources) for a gene, optionally filtered by drug. It does not explicitly contrast with sibling tools like lookup_gene or lookup_variant, but the drug-gene focus is apparent.
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 gives a conditional usage tip: 'Omit drug to list every drug curated for that gene,' which explains when to omit the drug parameter. However, it does not say when to prefer this tool over lookup_gene or lookup_variant, nor any exclusions or prerequisites. Usage is implied by the purpose.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Everything curated about one gene: function, key variants with rsIDs, metabolizer or function phenotypes, drug interactions, what a consumer genotyping array can and cannot tell you, and sources.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | HGNC gene symbol, e.g. CYP2C19. Case-insensitive. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint, idempotentHint, and destructiveHint, so the safety profile is covered. The description adds useful detail about the breadth of content, including variants, phenotypes, and array limitations, but does not disclose edge-case behavior such as handling of unknown gene symbols or response formatting. This adds moderate value beyond the 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, well-structured sentence that front-loads the core idea ('Everything curated about one gene') and then uses a colon-delimited list to enumerate specific content categories. There is no filler or redundant wording, and every segment contributes useful information.
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?
For a simple read-only, one-parameter lookup with no output schema, the description gives a solid account of what the agent can expect in the response, including a notable nuance about consumer genotyping array limitations. It does not mention invalid-symbol behavior or explicitly contrast with sibling tools, but these are minor gaps given the straightforward schema and sibling names.
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, 'symbol', is fully documented in the schema with an example ('CYP2C19') and case-insensitivity. The description adds no additional meaning about the parameter, so the schema carries the full burden. This matches the baseline for high schema coverage.
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 specifies the tool's purpose: retrieving curated information about one gene, and enumerates content types (function, variants, phenotypes, drug interactions, array limitations, sources). It focuses on a single gene, which distinguishes it from siblings like list_genes and lookup_variant, but it does not explicitly name those siblings or state how it differs from them.
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 the tool should be used when an agent needs comprehensive gene-centric curated data. However, it does not explicitly state when to choose this over lookup_variant or lookup_drug_gene, nor does it provide any exclusions or alternative routing. The agent must infer usage from the tool name and sibling context rather than from explicit guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
One variant by dbSNP rsID: gene, star-allele or common name, alleles, functional effect, population frequency and sources.
| Name | Required | Description | Default |
|---|---|---|---|
| rsid | Yes | dbSNP identifier, e.g. rs4244285. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare the read-only, idempotent, non-destructive safety profile. The description adds useful detail about returned data (gene/name, alleles, effect, frequency, sources) but does not reveal edge behavior such as unknown-rsID handling.
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?
One compact, front-loaded sentence carries the tool's scope and return contents with no filler or repeated structured data. It is efficient and easy to scan.
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?
For a single-parameter read-only lookup with no output schema, the description adequately communicates the input and the main categories of returned information. Nothing needed to select or invoke the tool correctly is missing.
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 schema fully documents the single required parameter, including an example format, so the description need not add param-level detail. The mention of dbSNP rsID in the description merely reinforces 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 uses a specific lookup verb and resource ('One variant by dbSNP rsID') and enumerates what is returned, so an agent can tell it apart from gene-listing and gene-lookup siblings. It is not a tautology and carries concrete scope.
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 clearly conveys the selection criterion: use this for exactly one variant identified by a dbSNP rsID. It stops short of naming sibling alternatives or explicit when-not conditions, but the context is unambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
list_geneslookup_drug_genelookup_genelookup_variantdbSNP refSNP records and HGVS/SPDI/rsID normalization for human genetic variants, from NCBI…
Search and share cited agent findings. Public reads; authenticated writes.
Read-only U.S. lab-test catalog, collection-site search, and reference-range context.
Linked life-science search: 40M+ papers, 1.2M+ trials, drugs, genes, FDA/EMA approvals, patents.
Add one secure layer between your agents and this server.
