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mediora__analyze_lab_pdf

Upload a lab-report PDF or scan (by HTTPS URL) and trigger Mediora.AI's full Vision + analysis pipeline. Returns a pending test id the client can poll. The URL must be https, must resolve to a public IP (no localhost / cloud-metadata / private LAN), and the file must be PDF / JPEG / PNG ≤ 10 MB. Redirects are not followed — pass the final URL.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
file_urlYesPublic HTTPS URL of the lab-report PDF to analyze.
languageNoPreferred language for the analysis output. Defaults to 'en'.
bearer_tokenYesMediora.AI patient JWT (see mediora__whoami).

Schema Changelog

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

  1. First observed

TDQS

A4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full burden. It discloses that the pipeline is asynchronous (returns pending ID for polling) and lists constraints. However, it does not mention error handling, authentication flow beyond bearer_token, rate limits, or whether the analysis is destructive. The description provides some behavioral traits but misses several important aspects.

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 four sentences, front-loaded with the main action and result, followed by constraints. Every sentence is informative and necessary. No redundant or vague wording.

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

Completeness4/5

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

Given the tool's complexity (async analysis with constraints, polling), the description covers the key aspects: what it does, URL requirements, and output. It lacks guidance on how to poll the returned ID or what to do on failure, but the sibling tools can fill that gap. Overall, it is fairly complete for a tool returning an ID.

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

Parameters3/5

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

Schema description coverage is 100%, so the baseline is 3. The description reinforces the file_url parameter with additional constraints (public IP, no redirects, file type/size) and clarifies the bearer_token's origin (from mediora__whoami). It adds value but does not significantly expand meaning beyond the schema for the language parameter.

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 states it uploads a lab-report PDF via URL and triggers an analysis pipeline, returning a pending test ID. It specifies the resource (lab-report PDF), action (upload/trigger), and output (pending test ID). This clearly distinguishes it from sibling tools which are explanation or listing tools.

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 provides explicit requirements: HTTPS URL, public IP, file type/size limits, no redirects. It implies usage scenarios (when you have a compliant PDF/JPEG/PNG) but does not explicitly state when not to use it or mention alternatives like other tools. However, the constraints serve as strong usage guidance.

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.