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Get design tokens

well_get_design_tokens
Read-only

Get Well's colours, shape and type vocabulary, so a view you compose for Well data looks like Well rather than a generic page.

Call this ONLY when you are about to render something yourself — an HTML artifact, a report, a chart you are drawing. You do not need it to answer in prose or in a markdown table.

Do NOT use it to restyle a card a Well tool already drew. Where a tool ships its own card the host renders it, and a second styled copy of the same figures is a duplicate, not an improvement.

Returns colors (roles, not raw token names — page_background, card_surface, text_primary, accent, positive, negative, ...), series (categorical chart colours in the order to consume them), shape (corner radius and gap), fonts, and color_scheme, which tells you which ground to compose against. When it is absent the stylesheet did not declare one — pick a ground from page_background rather than assuming.

Values come from the same token package the Well app, the browser extension and the tool cards compile against, so they cannot drift from the product.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
workspace_idNoTarget workspace. Optional: this tool describes the token itself rather than one workspace's data, so omitting it returns the same answer.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
errorNo
fontsYes
hintsNo
shapeYes
colorsYes
seriesYes
successYes
color_schemeNo

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark this read-only, and the description adds substantial behavioral context: the exact return fields and their semantics, the note that colors are roles rather than raw token names, the consumption order for series, and the fallback behavior when color_scheme is absent. It also explains the token source cannot drift from the product.

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 somewhat long but every sentence earns its place: purpose, usage precondition, exclusion, return-field semantics, and provenance. It is front-loaded with the core purpose and then layers necessary detail without 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?

The description fully equips an agent to decide when to call the tool, what to expect, and how to interpret edge cases such as missing color_scheme. An output schema exists, so the explicit return-field details are a bonus, not a necessity. Nothing material 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 already provides 100% coverage for the single optional workspace_id parameter, describing it as optional and stating omitting it returns the same answer. The main description adds no further parameter-level meaning, which is acceptable because the schema handles it; baseline 3 is appropriate.

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 Well's colours, shape and type vocabulary'. It clearly differentiates the tool from all sibling data tools by positioning it as the design-token source for custom rendering, not for data retrieval.

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?

Usage guidance is explicit: 'Call this ONLY when you are about to render something yourself' and 'Do NOT use it to restyle a card a Well tool already drew.' It gives concrete conditions for use and non-use, making the agent's decision unambiguous.

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

A4.5/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: CRUD for companies, people, and invoices; financial analytics (cash, cost, runway, holdings); connector management and invocation; schema discovery; querying; reconciliation; and contact channel management. No two tools could be confused for the same action.

Naming Consistency5/5

All tools follow the `well_verb_noun` pattern with consistent verb choices (create, get, list, update, delete, add, remove, run, resolve, query, invoke). The naming is predictable and makes the tool's purpose immediately clear.

Tool Count4/5

With 26 tools, the set is slightly above the ideal 3-15 range, but every tool earns its place given the breadth of the domain (CRM, invoicing, financial analytics, reconciliation, connector management). The count is well-scoped and not excessive.

Completeness4/5

The tool surface covers core CRUD, financial KPIs, reconciliation, and connector management. Minor gaps exist (e.g., no direct tool to update contact channels or manage accounts), but the query and schema tools allow agents to work around them, and the primary workflows are fully supported.

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