Lotus — AI Citation Intelligence

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

No annotations are provided, so the description bears full responsibility. It discloses rate limits for unauthenticated use and what the full analysis includes ('metrics, bleed model, simulations'), which is good. Missing details on the exact nature of these results, but still informative.

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 sentences with no filler. The first sentence states the main purpose, the second details the two modes. Every word 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?

The description covers behavior and rate limits but lacks details on output format, error handling, or domain validation. Given the tool has only 2 params and no output schema, it is minimally adequate but could be more complete (e.g., 'returns a JSON object').

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 add parameter meaning. It explains the api_key parameter's effect (triggering full vs. limited analysis). The domain parameter is only mentioned by name, not elaborated. Thus, partial compensation.

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 analyzes the GEO state of a domain, and distinguishes two modes based on api_key. Sibling tools are different enough that confusion is unlikely, though no explicit differentiation is given.

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 explains when to use api_key vs. not (limited preview vs. full analysis) and mentions rate limits. However, it does not provide guidance on when to use this tool over sibling tools, leaving it implicit.

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?

No annotations provided, so description carries full burden. It implies a read operation ('returns') but does not disclose side effects, pagination, or detailed behavior beyond the basic retrieval.

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 clear front-loading: first states purpose, second adds requirement. No filler or redundancy.

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 no output schema and no annotations, the description is thin. It omits return structure, error handling, and pagination, which are important for a tool returning data.

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 description adds meaning: 'domain' is implicit, 'api_key' requires specific plan types. This partially compensates, but lacks format or source details.

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 returns competitor actions for a domain, using a verb-resource pair. It distinguishes from siblings (analyze_geo, get_quick_wins) by focusing on actions, but does not explicitly differentiate.

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 explicit guidance on when to use this tool versus siblings. The description mentions api_key requirements but lacks context for when-not or alternative tools.

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?

With no annotations, the description must fully disclose behavior, but it only mentions returning data and an auth requirement. It does not state whether the tool is read-only, side effects, rate limits, or error conditions, leaving significant gaps.

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 concise with two short sentences, no redundant information, and all content is relevant. Every word serves a 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?

Given no output schema and only basic input schema, the description needs to explain what 'quick wins' are, the output structure, and any limitations. It fails to do so, leaving the agent with insufficient context for correct invocation and result interpretation.

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 0% description coverage, but the description adds value by specifying that api_key must be from pro, growth, or agency plans. However, it does not elaborate on the domain parameter format or any constraints, so it partially compensates.

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 that the tool returns pending and applied quick wins for a domain, using a specific verb 'Retorna' and resource 'quick wins'. It differentiates from sibling tools (analyze_geo, get_competitor_actions) by focusing on a distinct concept, though it does not explicitly contrast with them.

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 specifies a prerequisite: a valid api_key from pro, growth, or agency plans. However, it does not provide guidance on when to use this tool versus alternatives or when not to use it, leaving the agent to infer context.

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