List every clinical condition in the Mediora catalog. Returns slug + name + ICD-10 code per entry. Use mediora__explain_condition for the full explainer.
| Name | Required | Description | Default |
|---|---|---|---|
| lang | No | Language code. Defaults to 'en'. |
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?
Describes return fields but does not disclose whether the list is complete, paginated, or any additional behavioral aspects like read-only. Given no annotations, more detail could improve transparency.
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, no extraneous information. Each sentence adds value: first states purpose and output, second provides usage guidance.
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?
Adequately describes purpose, output, and alternative for a simple list tool with one optional parameter. Return values are stated, so lack of output schema is compensated.
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 covers the only parameter (lang) with enum and description, so baseline is 3. Description does not add further context about the parameter's behavior or defaults 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?
Clearly states it lists clinical conditions from the Mediora catalog and specifies return fields (slug, name, ICD-10). Differentiates from sibling 'mediora__explain_condition' by noting that tool provides full explainer.
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?
Explicitly directs to mediora__explain_condition for full details, indicating when not to use this tool. However, does not provide more exhaustive when-to-use or when-not-to-use scenarios.
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.
