blockrun_search

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

Annotations (readOnlyHint=true, destructiveHint=false) are consistent. The description adds significant behavioral detail: pricing formula, default cost, the effect of max_results on spend, source subset behavior, and default values. This fully informs the agent of the tool's cost and operational characteristics.

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: first paragraph gives purpose and cost warning, second explains common shape, third details sources and max_results. It is concise yet complete, with no unnecessary words.

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?

The description covers input thoroughly and hints at output (summarized results with citations). It references an external file for full examples, which is acceptable. However, a brief summary of the return structure would improve completeness, especially since no output schema is provided.

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%, but the description adds crucial context: the body structure example, default values, effect of max_results on cost, and the reserved nature of path. This goes well 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?

Description clearly states it performs 'Grok Live Search' across web, X/Twitter, and news with AI-summarized results and citations. It distinguishes itself from sibling tools like blockrun_markets or blockrun_chat by specifying the search domain and output format.

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 gives explicit guidance on when to use the tool (real-time search) and how to control cost (max_results). It does not directly compare to siblings, but the sibling names are sufficiently distinct that no confusion arises. The pricing warning and source selection advice help the agent decide parameters.

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