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mediora__whoami

Validate a Mediora.AI patient Bearer token and return the patient id + email it represents. Use this first to confirm the token before calling other auth-gated tools. The token is obtained by going through the standard Mediora.AI login at https://www.mediora.ai — the same JWT the browser stores in localStorage as 'auth_token'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bearer_tokenYesMediora.AI patient JWT. Get it by signing in at https://www.mediora.ai then copying localStorage.auth_token from DevTools, OR pass-through from a desktop client that stores Mediora credentials.

Schema Changelog

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

  1. First observed

TDQS

A4.8/5.0
Behavior4/5

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

With no annotations, the description fully describes the tool's behavior: it validates a token and returns id+email. It also explains token source. Missing details like error handling or what happens with invalid tokens, but overall transparent enough for an auth validation tool.

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: the first states the core function and output, the second provides usage guidance and token source. Extremely concise with no wasted words, front-loading the most important information.

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?

Given the tool's simplicity (single parameter, no output schema), the description covers everything needed: purpose, token retrieval, and when to use. It is fully complete for an authentication validation tool.

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?

The schema already gives 100% coverage with a description for the bearer_token parameter. The tool description adds significant value by explaining exactly how to obtain the token (browser DevTools or desktop client), which is crucial context beyond the schema.

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 clearly specifies the tool validates a Mediora.AI patient Bearer token and returns the patient id and email. It uses a specific verb+resource pair, distinguishing it from all sibling tools that are for analysis, explanation, or lookup.

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 states to use this tool first to confirm the token before calling other auth-gated tools, providing clear timing and purpose. It also explains how to obtain the token, though it does not explicitly discuss when not to use it, but the context makes it obvious.

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.