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

The description discloses several behavioral traits beyond the readOnlyHint annotation: it explains the resolution behavior (exactly one component vs. error listing available names), the ambiguity response structure ({ ambiguous, matches, instruction }), and the nature of the data ('static source-site facts, never runtime frequency'). It also clarifies that detail is opt-in and what each detail option returns. The only minor gap is not explicitly stating pagination behavior for limit/offset, but the schema covers those parameters.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but well-structured. It front-loads the core purpose and read-only nature, then explains resolution behavior, detail options, ambiguity handling, and usage guidance. Every sentence adds value, though it is somewhat long. The structure is logical: purpose → behavior → parameters → usage → alternative. It earns a 4 for being comprehensive without being bloated.

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

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

For a tool with 5 parameters, no output schema, and no schema descriptions, the description covers all essential aspects: what it does, how to use it, what the return values look like (component JSON, error, ambiguity object), what each detail option returns, and when to use an alternative. The ambiguity resolution instruction is particularly valuable for an agent. Nothing critical is missing for an agent to invoke this tool correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It does: it explains the 'context' parameter ('Pass context (your current working file or directory) so same-name components resolve by path scope'), the 'detail' parameter (lists all enum values and their meanings), and the 'name' parameter (lookup by name or id). It doesn't explicitly explain 'limit' and 'offset', but those are standard pagination parameters with clear schema constraints. The description adds significant meaning beyond the raw schema.

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

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

The description clearly states the tool's purpose: 'Look up a component by name or id' and distinguishes it from siblings by noting 'For a list of all components, use get_design_context with category 'components' instead.' It also specifies the resource (component) and the action (look up), making it easy for an agent to understand what this tool does.

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

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

The description provides explicit guidance on when to use this tool ('Use this when you need implementation details for a known component to reuse it rather than recreate it') and when not to use it ('For a list of all components, use get_design_context with category 'components' instead'). It also explains the ambiguity resolution process, which is a clear usage guideline for handling multiple matches.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true, and the description reinforces this with 'Read-only, no side effects' while adding useful behavior: bounded, repeatable pages with pagination defaults. It does not describe sorting or response shape, but for a read-only list tool the disclosed behavior is sufficient.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences with no filler: purpose, safety, filters, pagination, and alternatives. The most important facts are front-loaded, and each sentence earns its place.

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

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

For a 7-parameter read-only paginated endpoint with no output schema, the definition covers the core invocation needs: filters, pagination behavior, and when to choose different tools. It leaves out explicit return details and the relationship to get_violations, but an agent can safely invoke the tool based on this description.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description compensates well: it explains type/status/scope filters, clarifies that component is an exact name or durable ID, describes fieldPath as a structured prefix, and gives pagination defaults. Every parameter in the schema is given some operational meaning.

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

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

States a specific verb and resource: 'Get a bounded, repeatable page of design-system conflicts.' It also routes resolved-value lookups to get_token/get_component, but it does not clearly distinguish this from the sibling get_violations, which could overlap in meaning.

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

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

Provides clear context and pagination instructions ('Use offset and limit for pagination, default 25, maximum 100') and explicitly says to use get_token or get_component for resolved design values. It does not, however, say when get_violations or get_inferred_rules would be more appropriate than this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Although readOnlyHint=true is already in annotations, the description adds substantial behavioral context beyond it: the default return shape, the category-detail behavior, tokenCategory filtering semantics, and notably the error behavior ('unknown/aliased categories return an actionable error, not a silent empty result'). It also reinforces 'Read-only, no side effects,' which aligns with the annotation. No contradiction.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is information-dense with a logical flow: purpose, safety disclaimer, default return, category options, tokenCategory filter with error behavior, usage positioning, and alternatives. Every sentence earns its place, though the parameter enumerations make it longer than necessary. Well front-loaded with the core purpose.

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

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

Comprehensive for an overview tool: default return contents, category values, tokenCategory values, error behavior, and usage positioning are all covered, with no output schema to fall back on. The main gap is pagination semantics for limit and offset, which remain unexplainable given the 0% schema coverage.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, so the description carries the full burden, and it compensates well for two parameters: category (explicit value list: 'all' | 'tokens' | 'components' | 'conflicts' | 'diagnostics') and tokenCategory (exact filter values: colors, spacing, sizes, typography, borderRadius, shadows, zIndex, breakpoints, motion). However, limit and offset are completely unaddressed in both the bare schema (only type/min/max) and the description, leaving half the parameters undocumented.

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

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

States a specific verb and resource: 'Get the resolved design system context before building UI.' It clearly explains what the tool returns (a summary of token counts, component names, conflict counts, diagnostics, and contract metadata) and explicitly differentiates from siblings by noting 'For lookups by name, use get_token or get_component instead.' An agent can immediately distinguish this overview tool from the lookup tools.

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

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

Gives explicit when-to-use guidance: 'Use this as the first call to understand what exists.' It also provides clear when-not-to-use direction with named alternatives ('For lookups by name, use get_token or get_component instead'). This is exemplary routing behavior that leaves nothing to inference.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Annotations already declare readOnlyHint=true, and the description repeats that fact. It adds useful behavioral context beyond the annotation: returns a JSON list with category/pattern/confidence, errors if no rules have been generated, and supports filtering by category.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is dense but front-loaded, with every sentence adding value: purpose, safety, return shape, filter usage, and alternative tools. No wasted words or repetition of schema-only details.

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

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

The tool is simple (one optional parameter, no output schema), and the description covers the return format, error case, filter values, and usage context. It is fully adequate for correct tool invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0%, but the description fully compensates by enumerating exact accepted values: spacing, colors, typography, borderRadius, naming, components. It also explains that omitting the parameter returns all rules.

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

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

The description uses a specific verb and resource: "Get the design rules inferred from your codebase patterns." It clearly differentiates from siblings by naming get_token and get_conflicts as alternatives for other intents.

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

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

Explicitly states when to use the tool ("understand implicit conventions") and provides exclusions: "For explicit design token values, use get_token. For source conflicts, use get_conflicts." Also explains category filtering and omission behavior.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

Although annotations already declare readOnlyHint=true, the description adds meaningful behavioral detail: 'Read-only, no side effects,' explains the return value, and discloses the error condition when the token is not found. It also explains alias normalization for categories, which goes beyond the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is composed of three well-structured sentences, with the primary action and read-only guarantee front-loaded, followed by return/error behavior, then category semantics and sibling-tool guidance. Each sentence earns its place, though the category-alias list makes it slightly denser than strictly necessary.

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

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

For a simple two-parameter look-up tool, the description is complete: it covers input semantics, return behavior, error outcome, category normalization, and alternative tool usage. No output schema is present, but the description explicitly lists what the return contains and the error case, so the agent can predict behavior.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The input schema has zero description coverage, so the description carries the full burden of explaining parameters. It explains that 'name' is the token name to search for and 'category' narrows the search, listing all valid categories and alias normalization behavior. This adds substantial meaning beyond the raw schema.

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

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

The description clearly states the tool's purpose: 'Look up a specific design token by name' and specifies the exact resource (design tokens), the return value, and the error behavior. It is distinguished from sibling tools like get_design_context by explicitly targeting single-token lookup vs broad overview.

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

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

The description gives clear when-to-use guidance: 'Use this when you know the token name.' It also provides an explicit alternative for a different use case: 'For a broad overview of all tokens, use get_design_context with category 'tokens' instead.' This is model guidance for tool selection.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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

The description states 'Read-only, no side effects,' which aligns with and expands on the readOnlyHint annotation. It additionally discloses return structure, filtering behavior, and the fact that hardcoded values are only detected for certain categories. This provides rich behavioral context beyond the annotation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficiently structured: it defines the topic, states safety, summarizes output, explains the parameter, and gives usage guidance. No sentence is wasted; it remains readable while packing substantial information.

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

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

Even without an output schema, the description thoroughly details the return JSON structure (count, suggestion-coverage stats, list with file:line:column, literal, utility, smart-match suggestion). It covers the parameter, use cases, and alternatives, making the tool fully understandable for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 0% schema description coverage, the description fully compensates by explaining that the `category` parameter filters results and lists allowed values ('all' | 'colors' | 'spacing'), also noting why these categories matter. This adds essential meaning not present in the schema.

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

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

The description clearly states the tool's function: getting hardcoded token values (literals in source code instead of design tokens). It distinguishes itself from siblings by explicitly referencing get_design_context and get_token as alternatives for available tokens, and by describing its specific output related to violations.

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

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

The description provides explicit usage guidance: 'Call this BEFORE generating UI with literal values — prefer the suggested token over a hardcoded literal.' It also names alternative tools for token lookup, giving clear when-to-use vs. when-not-to-use direction.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.