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Get Consent Text

consent_text
Read-only

Fetch the full text of a specific consent document for patient review. Returns the complete consent document split into titled sections that the agent MUST present to the patient verbatim in the conversation — do not summarize or paraphrase. Includes: consent version number, effective date, section headings and body text, a confirmation prompt the patient should agree to, and withdrawal instructions. Available consent types: telehealth informed consent, compounded medication treatment consent, pharmacy authorization, HIPAA notice of privacy practices, and AI-assisted intake disclosure. The patient must explicitly confirm each consent before the agent can call consent_submit. Requires authentication.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
consent_idYesConsent document ID from consent_list
bearer_tokenNoAuthentication token for the patient session

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.1/5.0
Behavior4/5

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

Annotations declare readOnlyHint=true, so the read-only nature is already known. The description adds significant behavioral context beyond that: it returns the consent split into titled sections, must be presented verbatim, includes version/effective date/content/confirmation prompt/withdrawal instructions, lists available consent types, and notes authentication requirements. It also clarifies the prerequisite for calling consent_submit, which helps the agent understand the operational context. No contradiction with annotations.

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 moderately long but every sentence adds value: it states the purpose, mandates verbatim presentation, lists content elements, enumerates consent types, and gives a workflow constraint. It is structured with clear sentences, front-loading the core purpose before elaborating. No unnecessary fluff or repetition.

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?

The tool returns structured consent text and there is an output schema (indicated by the context signal), so explaining return values is not necessary. The description covers the key contextual aspects: what the returned data contains, how it should be used (verbatim presentation), what consent types are available, and the workflow prerequisite for consent_submit. Authentication is also mentioned. This is sufficiently complete for an agent to use the tool correctly.

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 both consent_id and bearer_token are already described in the schema. The description does not add parameter-level details beyond the schema. The mention of available consent types might help the agent understand what consent_id refers to, but since consent_id is an opaque ID from consent_list, the description does not materially enhance parameter understanding. Baseline 3 is appropriate due to high schema coverage.

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 'Fetch the full text of a specific consent document for patient review,' which is a specific verb+resource. It distinguishes itself from siblings like consent_list (which lists IDs), consent_status (checks status), and consent_submit (submits confirmation). The explicit mention of returning sections and the verbatim presentation requirement further clarifies its unique role.

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 usage context: it should be used to fetch consent text for patient review, and the agent must present it verbatim. It also states a workflow rule: the patient must confirm each consent before calling consent_submit, which helps the agent sequence tools correctly. It does not explicitly state 'when not to use,' but the alternative is implied by the sibling list and the mention of consent_submit.

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

A3.9/5.0
Disambiguation5/5

Every tool targets a distinct resource and action within the telehealth workflow: auth, consent, eligibility, intake, medications, checkout, order, portal, and provider communication. Even the multiple medication tools (list, details, availability, pricing) have clearly separated purposes, and there is no overlap among the 34 tools.

Naming Consistency5/5

All tool names follow a consistent `domain_verb` or `domain_noun` snake_case pattern, prefixed by their domain (auth_, checkout_, consent_, intake_, medications_, order_, portal_, provider_). There are no mixed conventions or vague verbs, making the API predictable and easy to navigate.

Tool Count2/5

With 34 tools, the server exceeds the 25+ threshold for 'too many' and feels fragmented. Many tools could be consolidated (e.g., medication pricing and availability could fold into details, and consent list/status could be combined). While the scope is broad, the count is excessive for a well-scoped MCP server.

Completeness4/5

The tool surface covers the full patient lifecycle from authentication and consent through eligibility, intake, checkout, order management, and post-order portal features. Minor gaps include lack of order cancellation or order listing, and no explicit intake update mechanism, but these are workable and do not block core workflows.

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