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Build an IR Viewer URL with a structured view recipe (draft contract, not yet read by the dashboard)

build_ir_view_recipe_url

Converts natural-language display requests into a structured recipe URL for the IR viewer. Validates and encodes the recipe as composable view directives.

Instructions

DRAFT / PROPOSED contract only -- dashboard.furiosa.dev does not read this recipe param yet. This exists so an AI agent can translate a loose display request ('bridge에 하이라이트 해줘', 'IO 연산은 파란색으로', 'latency 큰 operator 강조해줘') into a small, fixed set of composable view directives instead of re-deriving a full custom rendering from raw IR data on every request. The tool only validates and encodes the recipe as a query param; a deterministic renderer in the dashboard frontend (separate codebase) still needs to be built to read recipe and actually draw it. Until that exists, treat the returned URL's recipe param as inert and the response's recipe field as the artifact to hand to whoever implements that renderer.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
shaYesShort commit sha, first 10 hex chars
testYesExact test name
contextYesContext value, e.g. 'test-snapshot-8pe'
highlightNoOp/node categories to highlight, e.g. ['bridge']
compareModeNoWhich measurement to plot when this commit has both
emphasizeTopNNoHow many top operators to emphasize for emphasizeMetric
colorByCategoryNoColor nodes of this category distinctly and dim everything else (e.g. 'io' -> blue, rest gray)
emphasizeMetricNoVisually emphasize the operators with the highest value of this metric
annotateBufferTypeNoShow each tensor's buffer type alongside its name
Install Server

TDQS

A4.4/5.0
Behavior5/5

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

With no annotations, the description carries full behavioral disclosure. It explicitly warns that this is a draft/proposed contract, that the dashboard does not read the recipe param yet, that the tool only validates and encodes, and that the returned recipe field is the only artifact to hand to a future renderer implementer.

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 long but every sentence earns its place: draft status, intended use, tool behavior, and the inert-recipe warning are all essential. It is front-loaded with the DRAFT warning, though the dense paragraph style could be slightly more scannable.

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 no output schema and no annotations, the description provides all critical context: the contract's draft state, the tool's limited role, how to interpret the response's recipe field, and the relationship to the static dashboard renderer. An agent can safely call the tool without facing hidden surprises.

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 the schema already documents all 9 parameters including enum values and examples. The description adds overall framing about composable view directives but does not add per-parameter semantic detail beyond the schema.

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 title and description state a specific action: build a URL for the IR viewer containing a structured recipe. It clearly differentiates from siblings by highlighting that this tool converts loose display requests into a fixed set of composable directives, while other tools like get_ir_viewer_url likely serve stable direct URLs.

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 explains when to use this tool: when an AI agent receives a loose display request and needs to translate it into a structured recipe rather than deriving custom rendering from raw IR data. It does not explicitly name alternatives or say when NOT to use it, but the context is clear.

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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