support_helenaLearn how an agent can support or spread Helena's free public scientific infrastructure through the separate Helena Good MCP. This is an explicit opt-in information action. It does not initiate payment, create a relay, or change any Folklore scientific result.
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint=true, openWorldHint=false, idempotentHint=true, destructiveHint=false. The description adds specific behavioral context beyond these annotations: it explicitly clarifies this is an information action and names precise exclusions (no payment, no relay creation, no result modification). This enriches the agent's understanding of side effects and side-effect-free guarantees, without contradicting 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 two sentences with no wasted words. The first sentence front-loads the purpose and mechanism; the second sentence immediately addresses exclusions and behavioral boundaries. Each sentence earns its place, and the structure is efficient for agent consumption.
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 parameterless, read-only, informational tool with output schema present, the description covers all necessary aspects: what it does, how it works (via separate MCP), and what it does not do. Annotations handle safety/behavior, and the absence of parameters removes any parameter-related gaps. Nothing material is missing for an agent to correctly invoke this tool.
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 0 parameters, so the baseline for parameter semantics is 4. The description adds no parameter-specific information because none is needed; the schema is empty and coverage is 100%. The description's focus on purpose and behavioral constraints is appropriate for a parameterless tool.
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 verb 'Learn how an agent can support or spread' and the resource 'Helena's free public scientific infrastructure'. It explicitly identifies the mechanism ('through the separate Helena Good MCP') and distinguishes itself from the sibling search tools by focusing on support rather than search. The sentence 'This is an explicit opt-in information action' further clarifies the tool's nature, removing ambiguity about whether it performs actions.
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 provides clear exclusions: 'It does not initiate payment, create a relay, or change any Folklore scientific result,' which tells the agent when NOT to use this tool. It also states it is an 'opt-in information action,' implying it should be used when seeking informational support on spreading Helena's infrastructure. However, it does not explicitly contrast with sibling tools or state when to choose it over alternatives, though the context of sibling search tools makes this reasonably clear.
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 clearly distinct roles: evidence/classification, variant-specific literature, broad literature search, publication details, and an auxiliary support action. The main ambiguity is between search_literature_corpus and search_variant_literature, though their descriptions clarify that one is a general semantic search and the other resolves a specific variant's publications.
All tool names follow a consistent snake_case verb_noun pattern: get_publication_details, search_literature_corpus, search_variant_evidence, search_variant_literature, and support_helena. The naming convention is uniform and predictable.
Five tools is a well-scoped count for a clinical variant interpretation server. Each tool contributes to literature search, variant evidence, publication retrieval, or explicit support infrastructure, without unnecessary bloat.
The core workflow is covered: find literature, retrieve publication details, resolve a variant, obtain ACMG/AMP evidence, and find variant-specific publications. Minor gaps exist for batch variant handling or larger/structural variants, but within the stated GRCh38 SNV/indel scope the surface is reasonably complete.