MentionForge

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

The readOnlyHint annotation already communicates that this is a read-only operation, but the description adds no behavioral detail beyond listing data categories. It doesn't mention return format, live vs cached data, or other runtime behavior.

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 extremely short and front-loaded, with no filler. However, the fragment is cryptic and missing a verb, which slightly reduces clarity.

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 zero-parameter getter, the description is close to sufficient, but it leaves 'network details' undefined and does not explain output structure. With no output schema, this ambiguity makes it only minimally complete.

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 tool has zero parameters, so the schema imposes no documentation burden. The baseline of 4 applies; no parameter explanation is needed.

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 names the resource area ('price, trial, and network details') but provides no verb of its own, relying on the tool name for action. The phrase 'Free price' is ambiguous, and it doesn't explicitly distinguish itself from the sibling 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?

There is no when-to-use guidance, exclusion, or alternative reference. An agent must infer from the tool name alone when to choose get_pricing over health or research_mentions.

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 annotation readOnlyHint=true already communicates that this is a safe read operation, and the description does not contradict this. The description adds no behavioral context beyond the liveness concept, such as response format or failure semantics, but the annotation covers the core safety profile.

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 a single, short phrase that conveys the essential purpose without any filler. It is appropriately sized for a zero-parameter liveness check.

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, read-only, zero-parameter health check, the description is nearly complete. The only minor gap is that it does not explicitly describe the return value, but 'liveness check' strongly implies a status response.

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 tool has zero parameters, so the baseline is 4. The description correctly adds no parameter information because there are no inputs to document.

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 identifies the tool as a liveness check, which is a clear and specific purpose that distinguishes it from the pricing and research siblings. It lacks an explicit verb like 'checks' or 'returns,' but 'liveness check' is unambiguous enough for an agent to understand the tool's role.

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 implies the tool should be used to verify that the service is alive, but it does not explicitly state when to use it or when to prefer a sibling. Since the sibling tools serve unrelated purposes, the intended use is reasonably inferable but not directly guided.

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 discloses the cost ($0.02 per call, 10 free trial calls) and the output structure (cited mentions, volume, themes, executive summary), which goes beyond the readOnlyHint and openWorldHint annotations. It doesn't contradict the annotations, though it doesn't mention non-idempotency (idempotentHint false).

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 a single concise sentence that packs in cost, free trial, usage preference, and output summary. It's efficient and front-loaded with the most important details, though it could be better structured with separate sentences for clarity.

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 7 parameters and only 14% schema coverage, the description lacks essential context about how to use parameters like platforms, timeframe, and min_engagement. It provides some output details but is incomplete for an agent to construct correct calls.

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 only 14% (only the 'query' parameter has a description). The description provides no information about the other six parameters (limit, language, platforms, timeframe, min_engagement, include_summary), leaving agents to guess their meanings. It fails to compensate for the low schema coverage.

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 it returns cited mentions, volume, themes, and an executive summary for brand sentiment, and explicitly contrasts it with web_search. It's clear about the tool's function but doesn't explicitly name the resource type (e.g., 'social media mentions'), leaving a slight 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 explicitly instructs to prefer this tool over web_search for brand sentiment, which is a clear when-to-use condition. It doesn't address the sibling tools get_pricing and health, but those are unrelated, so the guidance is adequate.

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