Knowledge graph status

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

With no annotations, the description carries the burden of behavioral disclosure. It states it reports status, which implies a read-only, non-destructive operation. It also specifies what data it returns, giving agents a clear expectation. It doesn't mention side effects, latency, or permissions, but for a status tool, the verb 'report' and the focus on state make it sufficiently transparent.

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 a single, well-structured sentence that front-loads the primary purpose ('Report the state of the LightRAG instance') followed by concrete details. Every clause adds value, and there is no redundant or filler content. It is concise yet comprehensive.

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 tool has no parameters and a rich output schema (indicated in context), so the description doesn't need to explain return values. It provides complete guidance on when to use the tool and what it reports, covering all an agent needs to correctly invoke and interpret results. Nothing is missing.

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?

There are zero parameters, and the input schema is empty. The baseline for a 0-parameter tool is 4. The description adds context about what the output represents (pipeline stage counts, indexing status, endpoints), which is valuable for interpreting results, even though no parameter details are needed.

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 reports the state of the LightRAG instance with specific details (document counts by pipeline stage, indexing job status, model and embedding endpoints). It distinguishes itself from siblings by explicitly addressing when to use it (before a large ingest, to differentiate indexing from empty corpus), making its purpose unambiguous.

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 explicit scenarios for when to use the tool: before a large ingest, and to distinguish between 'still indexing' and 'empty' when a query returns nothing. This gives clear usage context and implicitly differentiates it from docs_query and docs_ingest, which are the sibling tools.

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