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mediora-mcp

by io.github.SLAtech-IL

mediora__explain_panel

Return the markers that make up a lab panel by slug — each resolved to its Mediora marker slug, name and one-line summary — plus the panel's search aliases (CBC/FBC, CMP, LFT, TFT, renal panel, …). Answers "what's included in a CBC / lipid panel / CMP". Follow each marker into mediora__explain_marker for full detail.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
langNoLanguage for the marker names/summaries. Defaults to 'en'.
slugYesPanel slug (e.g. 'complete-blood-count', 'lipid-panel', 'comprehensive-metabolic-panel'). Use mediora__list_panels to discover slugs.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Added

TDQS

A4.8/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It clearly indicates a read-only retrieval operation returning markers and aliases. However, it lacks details on potential errors, rate limits, or edge cases like invalid slugs. Still, the core behavior is well-described.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the primary action, no redundant words. Every sentence adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple tool with 2 parameters and no output schema, the description fully captures what the tool returns and how to use it. It connects to related tools (list_panels, explain_marker) to provide a complete usage context.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with both parameters described. The description adds meaning by providing example panel slugs and noting that lang defaults to 'en', and instructs the agent on how to discover valid slugs using a sibling tool.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Return the markers that make up a lab panel by slug' and specifies exact output fields (slug, name, summary, aliases). It clearly distinguishes from sibling mediora__explain_marker by saying 'Follow each marker into mediora__explain_marker for full detail.'

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly answers 'what's included in a CBC / lipid panel / CMP' with examples. Provides guidance to use mediora__list_panels to discover slugs and recommends chaining with mediora__explain_marker for deeper analysis.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4.4/5.0
Disambiguation5/5

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.

Naming Consistency4/5

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.

Tool Count5/5

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.

Completeness5/5

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.

Resources