splunk__lsp_call_chain

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

With no annotations, the description bears full responsibility. It discloses that the tool returns a JSON call chain or an error if repo_path is missing or symbol unresolved. It doesn't mention destructive behavior (none expected) and gives direction/depth options, providing good transparency.

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 concise with no wasted words. Each sentence serves a purpose: stating the core action, providing usage context, and detailing parameters. It is well-structured and front-loaded.

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 6 parameters, 2 required, and an output schema exists, the description adequately covers what the tool does and returns. It mentions error conditions. It could mention prerequisites like calling investigate_start first, but the error note indirectly addresses that.

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 0%, yet the description explains all 6 parameters: run_id, symbol, file_path, line, direction, and depth. It gives examples for symbol, explains direction values, default depth, and optional file_path/line, adding significant meaning beyond the schema.

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 purpose: tracing a function or symbol through the microservice call graph using LSP, and it is used during the Reason step. It distinguishes itself from siblings like splunk__get_findings and splunk__hint by focusing on call graph tracing.

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 instructs to use this tool during the Reason step to find code paths producing log errors. While it does not list when not to use or provide alternatives, the context is clear and sufficient for an AI agent.

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