summarize_function

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

No annotations are provided, so the description carries full responsibility. It discloses that results (summary, keywords, call edges) are stored in the graph, implying a write operation. It does not explicitly mention mutational behavior or required permissions, but the side effects are described sufficiently for an LLM agent.

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 three sentences long, each serving a distinct purpose: defining the action, specifying when to use it, and detailing the outcomes. It is front-loaded with the most critical information and contains 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, combined with the fully documented schema and the presence of an output schema, provides sufficient completeness. It covers the tool's purpose, when to invoke it, and what happens to the data, making it well-suited for an AI agent to select and use correctly.

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 100%, so the baseline is 3. The description adds context by referring to 'a single function,' which reinforces the function_name parameter's purpose. However, it does not elaborate on parameter details beyond the schema. This slight added value justifies a score of 4.

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 performs a deep-LLM analysis of a single function, producing a summary and call extraction. It distinguishes itself from siblings by explicitly stating the invocation order (after query_graph) and noting that results feed into other tools like get_callers and get_callees.

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 explicitly advises to call this tool after query_graph() to enrich specific functions, establishing a clear usage order and context. It also states that results become available to other tools, helping the agent understand the tool's role in the pipeline.

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