inspect-logs-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 describes what the tool does (get aggregate statistics) and the filtering parameters, but lacks behavioral details such as whether it's read-only, potential performance impacts, error handling, or output format. For a tool with 5 parameters and no annotations, this is a significant gap in transparency.

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 a clear opening sentence stating the purpose, followed by bullet-like details on statistics and a structured Args section. Every sentence adds value, though it could be slightly more front-loaded by integrating parameter semantics earlier for faster scanning.

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 complexity (5 parameters, no annotations, no output schema), the description is partially complete. It excels in parameter semantics but lacks behavioral context and usage guidelines. Without an output schema, it should ideally describe return values, but it does not, leaving gaps in understanding the tool's full behavior and output.

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 compensate fully. It provides detailed semantics for all 5 parameters in the Args section, explaining each parameter's purpose (e.g., 'Directory containing log files', 'Filter by task name (supports wildcards)'), including format hints (ISO format for dates) and functionality (wildcard support). This adds substantial value beyond the bare schema.

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 with specific verbs ('Get aggregate statistics') and resources ('across multiple evaluation runs'), and it distinguishes from siblings by specifying the type of statistics (summary statistics grouped by task and model). It explicitly lists what statistics are included (success rates, sample counts, token usage totals, duration averages), making it highly specific.

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 the sibling tools (e.g., tool_compare_runs, tool_get_eval_summary). It mentions filtering capabilities but does not explain alternatives or exclusions, leaving the agent to infer usage context without explicit direction.

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