SerpApi

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds substantial behavioral context beyond that: compact versus complete response modes, Markdown behavior, output wrapper structure, guided-search interaction for missing parameters, cancellation semantics, and failure wrapper behavior. This goes well beyond what annotations or schema alone provide.

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 long but well-organized with clear sections, bullet lists, and examples. The engine-discovery instructions are repeated twice, which is mildly redundant, but the overall structure is scannable and every major section earns its place given the tool's breadth.

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 broad, engine-flexible tool, the description is remarkably complete: it covers when to use the tool, how to discover supported engines, parameter conventions, response modes, output schema, guided-search behavior, and realistic examples. With a rich output schema and annotations present, no critical operational detail is missing.

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 100%, but the description adds meaningful guidance beyond the schema: it explains which parameters are common, notes 'q' is required for most engines, gives concrete JSON examples, and points to MCP resources for engine-specific parameter discovery. This helps an agent construct valid params far better than the raw schema alone.

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 states a specific verb and resource: a universal search tool covering 'all SerpApi engines and result types.' It clearly enumerates the kinds of live structured SERP data it retrieves. However, it never explicitly contrasts itself with the sibling tools search_dashboard and search_table, so the differentiation is implicit rather than stated.

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 'When to use' section explicitly lists concrete scenarios such as web results, news, product listings, flights, and videos, giving an agent clear selection criteria. It does not mention when not to use the tool or point to sibling alternatives, but the listed conditions are specific enough to guide invocation.

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 cover read-only, non-destructive, and open-world hints. The description adds behavior beyond these: it describes the interactive rendering in conversation and the automatic selection of engine-specific dashboards (e.g., google_flights), which is valuable context. No contradiction with annotations.

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 zero fluff. The key differentiator (dashboard variant) is front-loaded, followed by output details and usage guidance. Every 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 variant tool, the description is complete: it states what it returns, when to use it, and where to get parameter details. It does not mention pagination or error handling, but these are not critical for a read-only dashboard tool with annotations already covering safety.

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 schema has one opaque `params` object with 0% coverage. The description compensates by stating 'Same params as `search`', directing the agent to the sibling's schema for parameter details. This is a pragmatic and effective way to convey parameter 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?

The description clearly states it is an interactive dashboard variant of `search`, enumerating the specific outputs (summary metrics, source breakdown chart, results table with expandable detail panel). This distinguishes it from the sibling tools `search` and `search_table` without ambiguity.

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?

It says 'Use for a richer visual overview of a query's results', giving clear context for when to select this tool. It also notes 'Same params as `search`', implying the simpler `search` tool may be used for non-visual needs, though it does not explicitly name alternatives or exclusions.

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 and destructiveHint, covering the safety profile. The description adds useful behavioral context beyond that: results are organic, the table is sortable/searchable, it renders in the conversation, and it depends on MCP Apps support. No contradiction with annotations.

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 two sentences with no filler. The first sentence front-loads the core purpose and sibling relationship; the second gives the usage condition. Every clause 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 thin wrapper around `search`, the description covers purpose, differentiation, when to use it, and the parameter relationship. Annotations cover read-only safety, and there is no output schema to document. It only lacks explicit handling of what happens when the host lacks MCP Apps support, and its parameter semantics depend on the parent tool.

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 schema has a single opaque `params` object with additionalProperties true and 0% description coverage, so the description must compensate. Saying 'Same params as `search`' adds meaning by reference and tells the agent to reuse another tool's parameter definitions, but it does not enumerate or describe those parameters, leaving the payload underspecified.

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 behavior: it returns organic results as an interactive, sortable/searchable table rendered in the conversation. It explicitly positions itself as a 'variant of `search`', which distinguishes it from the sibling tool without needing to open any schemas.

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 a concrete use condition: 'Use when the host supports MCP Apps and the user wants to browse results visually rather than read JSON.' It points to the alternative `search`, but does not explicitly state when not to use this tool or address the `search_dashboard` sibling, so it stops short of fully explicit routing.

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