Toofi Dental Planning MCP

Get dental plan operation status

get_dental_plan_operation_status

Read-onlyIdempotent

Poll a Toofi headless treatment-plan operation until AI planning is completed and ready for presentation/PDF delivery.

Input Schema

TableJSON Schema

Name	Required	Description
`plan_id`	Yes	Toofi plan id returned by generate_dental_treatment_plan_pdf.
`agent_id`	No	Calling agent identifier.
`agent_key`	No	Toofi agent API key. May also be supplied as X-Toofi-Agent-Key header.
`clinic_id`	No	Clinic identifier for mandate-scoped production execution.
`intent_id`	No	Root agent intent id.
`mandate_id`	No	Clinic or agent mandate id. Optional in public demo mode.
`patient_id`	No	Patient id returned by generate_dental_treatment_plan_pdf.
`request_id`	No	Idempotency and correlation id echoed in Toofi responses.
`include_pdf`	No	When true, completed operations return a signed presentation PDF URL.
`operation_id`	Yes	Operation id returned by generate_dental_treatment_plan_pdf.
`presentation`	No
`principal_id`	No	Human or clinic principal on whose behalf the agent acts.
`session_token`	No	Alias for runtime_session_token.
`toofi_agent_key`	No	Alias for agent_key.
`runtime_session_token`	No	Toofi runtime actor token for service-user scoped execution. May also be supplied as x-toofi-session-token header.

Output Schema

TableJSON Schema

Name	Required	Description	Default
`ok`	No
`mode`	No
`status`	No
`endpoint`	No
`timestamp`	No

TDQS

A3.8/5.0

Behavior3/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive). The description adds the polling behavior ('Poll... until completed'), which implies a long-running or blocking operation, but does not disclose timeout, error handling, or response behavior. This adds some context beyond annotations but not rich detail.

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 a single sentence with a front-loaded verb and no filler. Every word contributes to the purpose and outcome.

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 (15 parameters, nested objects) and the presence of a rich output schema and annotations, the description sufficiently covers the high-level purpose and polling behavior. It could mention potential blocking nature more explicitly, but overall it is complete enough with the existing structured metadata.

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 93%, and the schema already provides meaningful descriptions for key parameters (e.g., 'Toofi plan id returned by generate_dental_treatment_plan_pdf'). The description adds no parameter-specific semantics, so the baseline 3 applies.

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 the verb 'Poll', the resource 'a Toofi headless treatment-plan operation', and the success condition 'until AI planning is completed and ready for presentation/PDF delivery.' This distinguishes it from generic status tools like get_status by specifying the operation type and goal.

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

Usage Guidelines3/5

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

The description implies the tool is used after an operation has been initiated, but it does not explicitly name the initiating tool (e.g., generate_dental_treatment_plan_pdf) or mention alternatives/exclusions. It provides context ('poll... until AI planning is completed') but lacks explicit when-to-use 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

B3.3/5.0

Disambiguation3/5

Several tools have overlapping purposes, particularly example_en, example_pl, example_ru, example_sk, example_ua, example_uk, and get_example_result, which all return example PDFs. Also, example_ua and example_uk are explicitly aliases for the same Ukrainian example, creating direct ambiguity. Core clinical tools are distinct, but the example/demo cluster muddies the boundary.

Naming Consistency4/5

Most tools follow a snake_case verb_noun pattern (e.g., list_patients, create_agent_checkout_session, generate_price_estimate), but a few deviations exist: example_en/pl/ru/sk/ua/uk lack a verb prefix, and get_example_result seems to duplicate example_en. The 'pano' abbreviation in start_pano_markup is also slightly inconsistent. Overall, the pattern is mostly predictable.

Tool Count2/5

With 32 tools, the count is too high for the apparent scope of dental planning. Many tools are redundant example/demo variants (e.g., 6 language-specific example tools plus get_example_result, and multiple demo getters/listers). This bloat suggests the tool set could be consolidated to a more focused 15-20 tools without losing core functionality.

Completeness3/5

The core workflow is covered: generating plans, retrieving patients/plans, pricing, and billing. However, there are notable gaps such as no update or delete operations for plans or patients, no create patient tool, and no way to modify pricing beyond import_price_csv. The demo tools partially compensate by offering sample data, but the production lifecycle is incomplete.

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