Paid MCP Metering

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

With no annotations, the description carries the full burden. It does add useful behavioral context by stating the output includes a synthesized answer plus cited sources and by noting the $0.01/call price via x402. But it does not mention potential latency, source limitations, or other operational effects beyond cost.

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?

Two short sentences cover the tool's purpose, output form, and pricing without extraneous content. The key functional trait, multi-source synthesis with citations, is front-loaded, and the pricing detail is neatly separated.

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?

Given the small parameter set and clear schema, the description is broadly sufficient to understand what the tool returns, but it omits guidance on when this tool should be chosen over its sibling web_search. The lack of annotations and output schema makes some additional operational context, such as expected response shape or timeout behavior, useful but not critical.

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 100%, so the schema already explains both parameters clearly. The description does not add new parameter-level semantic detail, which aligns with the baseline 3 rather than exceeding it.

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 identifies the tool as a multi-source research pass that produces a synthesized answer with cited sources, which meaningfully distinguishes it from the sibling web_search. It lacks an explicit verb such as 'perform' or 'run,' but the noun phrase still conveys the action and resource clearly.

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 phrase 'Advanced multi-source research pass' implies it should be used when deeper, multi-source synthesis is needed, but the description does not explicitly state when to prefer it over web_search or when web_search is a better alternative. Guidance is mainly embedded in adjectives rather than direct instructions.

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?

There are no annotations, so the description carries the full behavioral burden. It usefully discloses that results are live, ranked, include source URLs, and cost $0.001/call, but it does not mention rate limits, error behaviors, or caching expectations.

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?

Two concise sentences with no filler. The core function is front-loaded, and the pricing detail earns its place as an important operational consideration.

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 search tool, the description covers the basic inputs and output shape, but it lacks any orientation of when to select the deep_research sibling as an alternative. No output schema exists, so a bit more return-format detail could help, but the current description is minimally workable.

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 100%, so the baseline is 3. The description does not add any meaning beyond the schema for query or max_results parameters.

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 performs a web search and returns ranked live results with source URLs. It does not explicitly differentiate itself from the sibling tool deep_research, so it misses the top score for sibling distinction.

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?

No guidance is provided about when to use web_search versus deep_research. The 'live results' wording implies real-time queries, but there is no explicit when-to-use or when-not-to-use instruction.

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