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Toofi Dental Planning MCP

List treatment plans

list_plans
Read-onlyIdempotent

List Toofi treatment plans through agent-native mandate rails.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
plan_idNoPlan id for plan listing.
agent_idNoCalling agent identifier.
plan_refNoPlan reference for plan listing.
clinic_idNoClinic identifier for mandate-scoped production execution.
intent_idNoRoot agent intent id.
mandate_idNoClinic or agent mandate id. Optional in public demo mode.
request_idNoIdempotency and correlation id echoed in Toofi responses.
patient_refNoPatient reference for plan listing.
principal_idNoHuman or clinic principal on whose behalf the agent acts.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
okNo
modeNo
statusNo
endpointNo
timestampNo

Schema Changelog

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

  1. First observed

TDQS

B3.3/5.0
Behavior3/5

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

Annotations already declare read-only, idempotent, and non-destructive behavior, so the bar is lower. The description adds the 'agent-native mandate rails' phrase, which implies the operation is scoped by mandates (clinic_id, mandate_id, principal_id), a useful behavioral context. However, it does not disclose other traits like pagination, ordering, or error behavior – though the output schema may cover return values.

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, front-loaded sentence: 'List Toofi treatment plans through agent-native mandate rails.' Every word earns its place; there is no filler or repetition of structured data. It is appropriately concise for a read-only listing operation.

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

Completeness3/5

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

Given the tool's complexity (9 optional params, output schema, robust annotations), the description is adequate but incomplete. It does not explain what 'agent-native mandate rails' means or distinguish from sibling listing tools (list_demo_plans, list_patients). The phrase could confuse an agent without domain knowledge. The output schema covers return values, but usage context is missing.

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?

The input schema provides 100% coverage with descriptions for all 9 parameters, including purpose and optionality. The description adds no parameter-specific meaning beyond the schema. Per the rubric, baseline 3 applies when schema coverage is high, and the description does not need to compensate further.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb and resource: 'List Toofi treatment plans' – this is specific and distinct from siblings like get_plan (single plan) or list_demo_plans (demo data). The added phrase 'through agent-native mandate rails' hints at the access scoping, though it is jargon-y. No explicit sibling differentiation, but the core 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 Guidelines2/5

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

The description provides no guidance on when to use this tool versus alternatives. It does not mention that get_plan retrieves a single plan or that list_demo_plans is for demo data, nor does it state any prerequisites (e.g., mandate context). The agent must infer usage from the tool name and sibling list, which is insufficient.

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.

Resources