snapshot_llm_citations

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

No annotations are provided, so the description carries full burden. It discloses that the tool runs a multi-LLM citation check and persists results, but it does not mention important behaviors such as whether it is destructive, rate-limited, or requires specific permissions. It also omits side effects like overwriting previous snapshots.

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 extremely concise, with two sentences plus a note about defaults. It is front-loaded with the tool's purpose and efficiently provides necessary detail without fluff.

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 has an output schema (not shown), return values need not be explained. However, the description omits context about prerequisites (e.g., configured LLM engines), potential costs (multi-LLM queries can be expensive), or whether it can be run multiple times without harmful effects. For a moderately complex tool, this information would improve completeness.

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 add meaning. It explains that 'engines defaults to every configured LLM,' which adds value beyond the schema. However, it does not describe 'questions' or 'target_domain' beyond their names, which are fairly self-explanatory but could benefit from context (e.g., format or constraints).

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 action: 'Run multi-LLM citation check and persist the per-question results.' It distinguishes from siblings like 'multi_llm_citation_check' (which likely does not persist) and 'trend_llm_citations' (which builds on this snapshot).

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 says it 'Builds the dataset behind trend_llm_citations,' providing context for when to use it (when you need persist results for trending). It also notes that 'engines defaults to every configured LLM,' giving default behavior. However, it does not explicitly state when not to use it or name alternatives like multi_llm_citation_check.

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