Multilead Open API MCP Server

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions a prerequisite ('if you previously started a conversation with them'), which adds useful context about when the tool works. However, it doesn't describe other behavioral traits such as rate limits, authentication needs, error conditions, or what happens if the conversation hasn't been started. For a tool with no annotation coverage, this leaves significant gaps in understanding its operational 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 well-structured and appropriately sized, with clear sections for the main purpose, arguments, and returns. It uses bullet-like formatting for 'Args' and 'Returns,' making it easy to scan. However, the 'Args' section could be more concise by integrating parameter explanations directly, and the 'Returns' section is somewhat vague ('including name, headline, company, etc.'), which slightly reduces efficiency.

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 that there is an output schema (which should document the return structure), the description doesn't need to detail return values extensively. However, with no annotations, 0% schema description coverage for inputs, and three required parameters, the description falls short in fully compensating for these gaps. It provides basic purpose and some usage context but lacks crucial details on parameter meanings and behavioral constraints, making it incomplete for reliable 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?

The schema description coverage is 0%, meaning none of the three parameters (user_id, account_id, linkedin_user_id) have descriptions in the input schema. The description lists these parameters in the 'Args' section but only provides their names without explaining what they represent, their formats, or how to obtain them. This adds minimal value beyond the schema, as it doesn't clarify the semantics or usage of these IDs, leaving them largely 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?

The description clearly states the tool's purpose: 'Retrieve LinkedIn profile information for a specific user.' It specifies the verb ('Retrieve') and resource ('LinkedIn profile information'), making it easy to understand what the tool does. However, it doesn't explicitly differentiate from sibling tools like 'get_user_information' or 'connect_linkedin_account,' which could cause confusion about when to use this specific LinkedIn-focused tool versus other user-related 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?

The description provides some implied usage guidance: 'This returns profile information if you previously started a conversation with them.' This suggests a prerequisite (having an existing conversation) but doesn't explicitly state when to use this tool versus alternatives like 'get_user_information' or 'connect_linkedin_account.' It lacks clear exclusions or comparisons with sibling tools, leaving room for ambiguity in tool selection.

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