read_graph

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'reads' but doesn't clarify if this is a safe read operation, what permissions are needed, whether it's paginated or returns all data at once, or any rate limits. The description is too brief to provide meaningful behavioral context beyond the basic action.

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, efficient sentence: '전체 지식 그래프를 읽습니다'. It's front-loaded with the core action and resource, with no wasted words. However, it could be slightly more informative without losing conciseness, such as by hinting at the output type.

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 of a knowledge graph tool with no annotations and no output schema, the description is incomplete. It doesn't explain what 'reads' returns—e.g., nodes, edges, metadata—or any behavioral traits. For a tool that likely handles structured data, more context is needed to guide the agent effectively.

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?

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description doesn't need to add parameter semantics, and it appropriately doesn't mention any. A baseline of 4 is applied since no parameters exist, and the description doesn't introduce confusion about inputs.

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 '전체 지식 그래프를 읽습니다' (reads the entire knowledge graph) states a clear verb ('reads') and resource ('knowledge graph'), but it's vague about what 'reads' entails—whether it returns metadata, structure, or content. It doesn't distinguish from siblings like 'get_entity_names' or 'list_entities', which might overlap in purpose. This is a minimal viable description without specific differentiation.

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?

No guidance is provided on when to use this tool versus alternatives. Sibling tools include 'get_entity_names', 'list_entities', and 'search_nodes', which could serve similar purposes, but the description doesn't indicate when 'read_graph' is preferred or what context it fits into. This lack of guidance leaves the agent to guess based on tool names alone.

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