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Glama

Chia Health MCP Server

by health.chia

Get Intake Questions

intake_questions
Read-only

Get the medical intake questionnaire for the chosen medication(s). The questionnaire is product-aware: GLP-1 / weight-loss medications return weight-loss goals, GLP-1 history, and MTC/MEN2 screening; NAD+ and other longevity peptides return energy/sleep/stress/cognitive/delivery-method questions instead. If the patient wants more than one medication, pass the additional slugs in additional_medications — the server returns the UNION of section sets deduped by section key, so you ask each shared question exactly once.

How to present this to the patient

  1. PROGRESSIVE DISCLOSURE: walk through ONE section at a time. Wait for the patient's reply before moving to the next section. Do not paste the whole questionnaire in a single message.

  2. HONOR CONDITIONALS: each section and each question may carry a conditional_on predicate (e.g. {sex_assigned_at_birth: Female} on the Pregnancy section). SKIP any section/question whose predicate isn't satisfied. Don't ask males about pregnancy or perimenopause.

  3. QUIZ FORMAT: present every select / multi_select question as a short pick-list using the options array verbatim. The patient should be able to reply with a single choice, not a sentence. Reserve free text for *_details follow-ups.

  4. EASY FIRST: order sections from low-friction (goals, lifestyle, preferences) to high-friction (clinical history, MTC/MEN2, prior therapies). The provider sees all answers regardless of order asked.

  5. USE-AND-VERIFY: if you know answers from prior conversation context, pre-fill them in your draft, but read them back to the patient and get explicit OK before calling intake_submit. Never silently submit assumed values.

Returns two phases: (1) pre_checkout — eligibility / screening questions, collected and submitted BEFORE payment; (2) post_checkout — detailed clinical history, collected and submitted AFTER payment. Do not submit post_checkout answers before the patient has paid. A licensed US healthcare provider reviews both phases and makes all prescribing decisions.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
client_ipNoClient IP address for rate limiting
medicationYesPrimary medication slug (e.g. 'semaglutide', 'nad-injection', 'nad-nasal-spray')
additional_medicationsNoOptional list of additional medication slugs the patient also wants. Use this when the patient is ordering more than one product in the same visit (e.g. GLP-1 + NAD+) so the questionnaire is deduped.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A5/5.0
Behavior5/5

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

Annotations declare readOnlyHint and openWorldHint, but the description adds significant behavioral detail: two-phase return (pre_checkout/post_checkout), conditional_on predicates, union deduplication for multiple medications, and the requirement not to submit post_checkout answers before payment. This substantially exceeds the annotations' disclosure.

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 long but every sentence earns its place. It starts with a crisp definition, then adds necessary behavioral nuance, and the numbered presentation list is highly structured and scannable. There is no fluff or redundancy.

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 complexity (conditional logic, multiple phases, presentation rules) and that an output schema exists, the description is complete. It covers when to use, how to present, sequencing with payment, and provider review, requiring no further inference.

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?

Although schema coverage is 100%, the description adds meaningful semantics: it explains how the 'medication' slug determines question sets (GLP-1 vs. NAD+), and how 'additional_medications' triggers union/deduplication behavior. This goes beyond the schema's bare parameter descriptions and helps the agent choose correct values.

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 opens with a specific verb and resource: 'Get the medical intake questionnaire for the chosen medication(s).' It clearly distinguishes from siblings like intake_submit and intake_status by emphasizing retrieval of questions, not submission or status. It also explains product-aware behavior (GLP-1 vs. NAD+), further clarifying scope.

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?

The description provides an explicit 5-step protocol for presenting the questionnaire to patients, covering progressive disclosure, conditional logic, quiz formatting, section ordering, and verification before submission. It also names the alternative 'intake_submit' and clarifies sequencing. This goes well beyond implied usage.

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

A4.2/5.0
Disambiguation4/5

Most tools have distinct purposes with clear boundaries, such as auth_* for authentication, checkout_* for payment, and portal_* for patient portal actions. However, there is some overlap between checkout_status and auth_check_payment (both involve polling for payment status) and between portal_support and portal_message (both for communication), which could cause minor confusion.

Naming Consistency5/5

Tool names follow a highly consistent verb_noun pattern throughout, with clear prefixes grouping related tools (e.g., auth_, checkout_, consent_, intake_, medications_, order_, portal_, provider_). This predictable structure makes it easy to understand the tool set's organization and purpose.

Tool Count3/5

With 34 tools, the count is borderline high for a telehealth server, as it may feel heavy and complex for agents to navigate. While the tools cover a comprehensive workflow from authentication to patient portal, some consolidation (e.g., merging similar polling tools) could improve usability without losing functionality.

Completeness5/5

The tool set provides complete coverage for the telehealth domain, including authentication, eligibility checks, medication browsing, intake, consent, checkout, order management, and patient portal features. There are no obvious gaps; agents can handle the entire patient journey from start to ongoing care without dead ends.

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