Logpush MCP

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

No annotations are provided, so the description carries the full burden. It discloses that the tool returns aggregated statistics in a dict format, including specific metrics like request counts and error rate, which adds useful context. However, it doesn't cover behavioral traits such as rate limits, authentication needs, or potential side effects, leaving gaps for a tool with no annotation support.

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 appropriately sized and front-loaded, with the core purpose stated first. The 'Args' and 'Returns' sections add structure without redundancy. However, the inclusion of 'Returns' details might be slightly redundant given the output schema, but it's still efficient overall.

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 moderate complexity (2 parameters, no annotations, but with an output schema), the description is fairly complete. It explains the purpose, parameters, and return values. Since an output schema exists, it doesn't need to detail return values extensively, but it still provides a summary. There are minor gaps in usage guidelines and full behavioral context.

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%, so the description must compensate. It adds meaning by specifying the date format (YYYYMMDD) and environment options (production or staging), which are not in the schema. However, it doesn't fully document all parameters (e.g., default values or constraints), but it provides enough context to justify a score above baseline.

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: 'Get aggregated statistics for logs on a specific date.' It specifies the verb ('Get aggregated statistics'), resource ('logs'), and scope ('on a specific date'). However, it doesn't explicitly differentiate from sibling tools like 'get_errors' or 'search_logs', which might also retrieve log-related data, so it doesn't reach the highest score.

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 no guidance on when to use this tool versus alternatives. It doesn't mention sibling tools like 'get_errors' for error-specific data or 'search_logs' for detailed queries, nor does it specify prerequisites or exclusions. The usage context is implied but not explicitly stated.

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