Return the full explainer for a clinical condition by slug. Includes: what the condition is, the key markers used in diagnosis, common symptoms, and when the patient should seek clinical evaluation.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language code. Defaults to 'en'. | |
| slug | Yes | Mediora canonical slug (e.g. 'hba1c', 'ferritin'). Use mediora__list_markers / mediora__list_conditions to discover slugs. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the transparency burden. It explicitly discloses what the returned explainer contains and the lookup method (by slug), making the read-only, informational nature clear. It does not discuss errors or auth, but these are not critical for a simple retrieval tool.
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 front-loaded sentence that states the action and resource, followed by a compact list of included content. There is no filler or redundancy.
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?
There is no output schema, so the description's content list is necessary and sufficient for an agent to understand the return value. It covers the key aspects of the explainer (definition, markers, symptoms, when to seek care), omitting only minor details like localization behavior, which is already in the 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 schema description coverage is 100%, with both parameters fully documented (slug with examples and discovery tools, lang with enum and default). The description adds no additional parameter-specific semantics beyond 'by slug,' which is sufficient given the complete 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 verb ('Return') and clearly identifies the resource ('full explainer for a clinical condition by slug'). It enumerates the content included, which distinguishes it from sibling tools like explain_marker or explain_symptom.
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 signals that this tool is for condition-level explanations (not markers, panels, or symptoms), giving clear context. It does not explicitly name alternatives or exclusions, but the scope is unambiguous, and the schema adds guidance to discover slugs via list tools.
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.
Each tool targets a distinct resource or action: catalog list/explain tools are divided by entity type (condition, marker, panel, symptom), patient data tools separate history, details, and trend analysis, and analyze_lab_pdf/whoami have unique roles. No two tools could reasonably be confused.
The set overwhelmingly follows a verb_noun pattern (list_*, explain_*, get_*, analyze_lab_pdf, lookup_marker, whoami). The only deviation is 'longitudinal_trend', which is a noun phrase rather than an action verb; still clearly readable.
At 14 tools, the set is well-scoped for a domain that spans catalog browsing, patient data retrieval, and lab report analysis. Each tool serves a distinct purpose and none feel redundant.
The lifecycle is complete: authenticate (whoami), ingest a lab PDF (analyze_lab_pdf), retrieve patient history (get_patient_history), drill into details (get_test_details), and analyze longitudinal patterns (longitudinal_trend). The catalog is fully browsable with list_* and explain_* tools, and lookup_marker bridges aliases. No obvious missing operations.
