rug_pull_detector__check_server

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

With no annotations, the description carries full burden and discloses a critical side effect: it connects to a server and auto-captures a baseline (a state mutation). It also explains the autoBaseline toggle's consequence. It does not cover process-spawning/networking implications or error/timeout behavior when the server is unreachable.

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?

Three compact sentences, each earning its place: what it does, what counts as a rug pull, and the baseline-fallback behavior. The core action is front-loaded.

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?

It covers the operation and the important autoBaseline fallback well for a 7-param tool. Since there is no output schema, the description could say more about what the comparison returns (e.g., changed-tool list), but the core callable behavior is well captured.

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%, so baseline is 3. The description adds value beyond the schema by explaining the runtime consequence of autoBaseline=false (requiring an explicit baseline_server call first), linking the parameter to the sibling tool's workflow rather than just restating the default.

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 uses a specific verb+resource ('compare its current tools/list response against the stored baseline') and explicitly defines what a 'rug pull' is. It distinguishes itself from the sibling baseline_server tool by explaining the relationship between baselining and checking.

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?

It clearly specifies the baseline-existence condition ('If no baseline exists yet, one is captured automatically') and names the alternative path via 'set autoBaseline=false to require an explicit baseline_server call first'. It gives clear context but doesn't fully spell out when to prefer check_server over list_baselines or other auditors.

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