leadpipe-mcp

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

With no annotations provided, the description carries the full burden and effectively discloses the mutating side effect (status update to qualified/disqualified), the scoring range (0-100), and the threshold logic (>=60). It also notes the data points considered (job title, company size, industry, custom rules), though it could clarify error handling or idempotency.

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 consists of two efficient sentences with zero waste. The first sentence front-loads the core action (calculating the score) and its inputs, while the second discloses the critical side effect (status update), making every word earn 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?

Given the tool's mutation behavior and lack of output schema, the description adequately covers the status update side effect and scoring logic. However, it omits what the tool returns (presumably the score value) and does not address error cases (e.g., invalid lead_id) or the relationship to the config_scoring sibling for custom rules.

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% for the single lead_id parameter, establishing a baseline of 3. The description mentions job title, company size, and industry as scoring inputs, which provides context about what the tool evaluates, though it does not clarify whether these are fetched from the lead record (implied) or additional parameters (which they are not).

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 calculates an AI-powered qualification score (0-100) and updates lead status based on thresholds. It identifies the specific resource (lead) and action (scoring/qualifying), though it does not explicitly differentiate from siblings like lead_enrich or config_scoring.

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 usage context by mentioning the status update side effect (qualified/disqualified), suggesting it should be used when making qualification decisions. However, it lacks explicit guidance on when to use this versus lead_enrich (data augmentation) or config_scoring (rule setup), or prerequisites like lead existence.

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