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mediora-mcp

by io.github.SLAtech-IL

mediora__longitudinal_trend

Return the per-marker trajectory analyser output for the authenticated patient — HbA1c rising, eGFR falling, ferritin depleting, etc. Each finding includes the marker slug, kind, severity (Info / Notice / Important), trend per year, sample count, date range and a supportive headline + detail. Proxies https://api.mediora.ai/api/trajectory/warnings with the user's token forwarded as Authorization: Bearer.

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
Behavior5/5

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

No annotations are provided, so the description carries full burden. It explicitly describes the return structure (marker slug, kind, severity, trend per year, sample count, date range, headline + detail) and the auth mechanism (bearer token proxy). This fully discloses the tool's behavior without ambiguity.

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?

The description is two sentences long. The first sentence states the core functionality with examples, and the second adds detail on output and proxy. No unnecessary 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?

Given the tool's simplicity (one parameter, no output schema, no nested objects), the description is fully complete. It explains the purpose, the output structure, and the authentication method. No gaps remain for an agent to misuse the 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 single parameter 'bearer_token' is fully described in the schema. The description adds significant value by explaining how to obtain the token (DevTools or pass-through from desktop client) and why it is needed. This goes beyond the schema's minimal description.

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 verb 'Return' and the resource 'per-marker trajectory analyser output for the authenticated patient'. It provides concrete examples (HbA1c rising, eGFR falling) and details the output fields, distinguishing it from sibling tools that list or explain markers without trend analysis.

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

Usage Guidelines4/5

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

The description explains that the tool proxies a specific API endpoint and forwards auth tokens, giving context on how it works. It does not explicitly state when to use versus siblings, but the purpose is clear enough for an agent to infer it is for longitudinal trends. No exclusion criteria are mentioned.

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