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Alya Hub

by com.mydaughteralya

alya_symptom_check

Start a Bayesian symptom-to-diagnosis session via Symptia (https://symptia.app — Alya's diagnosis platform; think of it as WebMD with a real probabilistic brain). Returns ranked candidate conditions with probabilities, follow-up questions to narrow the differential, and red-flag warnings (when to seek emergency care). Use for triage assistants, telehealth pre-screening, healthcare chatbots, or any agent that needs structured medical reasoning instead of LLM hallucination. NOT a substitute for licensed clinical diagnosis. Premium ($0.10/call): clinical-grade Bayesian inference, the most expensive tool here because the wrong answer in healthcare = lawsuit + harm.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
complaintYesPatient's chief complaint in their own words (any language). Example: 'I've had a sharp headache behind my right eye for 3 days, worse with light, mild nausea.'

TDQS

A4.4/5.0
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 transparently states it uses Bayesian inference, returns probabilities and red flags, mentions pricing ($0.10/call), and highlights the importance of not relying on it for final diagnosis. No contradictions.

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

Conciseness4/5

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

The description is relatively concise for a medical tool, front-loading the main purpose and including essential details (URL, use cases, disclaimer, pricing) without being overly verbose. 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 complexity and lack of output schema, the description fully explains what the tool returns (ranked conditions, probabilities, follow-up questions, red flags). This is sufficient for an agent to understand the tool's capabilities and limitations.

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% for the single parameter 'complaint', which includes an example and notes any language. The description adds overall context (Bayesian reasoning) but does not add significant parameter-specific meaning 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 identifies the tool as starting a Bayesian symptom-to-diagnosis session via Symptia. It specifies the output (ranked conditions, probabilities, follow-up questions, red flags) and distinguishes it from LLM hallucination. There is no directly similar sibling tool, so purpose is unambiguous.

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 use cases: triage assistants, telehealth pre-screening, healthcare chatbots. It includes a disclaimer that it is not a substitute for clinical diagnosis. It does not explicitly mention when not to use it, but the context is clear enough for an agent.

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

B3.4/5.0
Disambiguation4/5

Most tools have distinct purposes and clear descriptions, but there is some potential confusion among the four Polymarket-related tools (categorize, edge, signals, top_traders) and among the multiple 'alya_' prefixed tools that query different data sources.

Naming Consistency3/5

Naming patterns are mixed: some tools use 'alya_' prefix, others use action-based names like 'batch_calibrate' or 'image_gen', and YouTube tools all start with 'youtube_'. The inconsistency in prefixes and verb styles makes the set less predictable.

Tool Count2/5

32 tools is high for an MCP server, and they span a wide, unrelated set of domains (Polymarket, YouTube, gemology, weather, earthquakes, health, celebrity, etc.), making the surface feel bloated and unfocused.

Completeness2/5

Each domain has incomplete coverage: Polymarket lacks trade execution, YouTube automation depends on external OAuth, health tools only offer diagnosis and drug interactions without follow-up, and other domains have minimal tooling. The server feels like a collection of one-off features rather than a coherent surface.

Resources