build_knowledge_graph

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

No annotations exist, so the description carries full burden. It does not disclose whether the graph is built on-the-fly, cached, or modifies underlying data. It also omits potential side effects, permissions needed, or performance implications. This is a significant gap for a tool that likely requires computation.

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 two sentences, front-loaded with the core action, and free of extraneous information. Every word contributes to clarity.

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 is moderately complex (3 parameters, no output schema) but the description does not explain what the output looks like (e.g., format, structure of nodes and edges). Agents need to know the return value to use results. This is a serious omission.

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% – all three parameters are described in the input schema. The description does not add any meaning beyond the schema's descriptions (e.g., 'entity_types' is already explained). It meets the baseline expectation but does not enhance understanding.

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 explicitly states the tool constructs a knowledge graph from entities and relationships, and explains the graph representation (nodes and weighted edges). This clearly distinguishes it from sibling tools that analyze or compute metrics on graphs.

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 suggests using the tool to understand knowledge structure and find connections, but does not specify when to prefer this over alternatives like analyze_graph_pagerank or detect_graph_communities. No exclusions or when-not-to-use guidance is provided.

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