get_agent_context

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 describes the output contents and suggests saving to a file, but doesn't explicitly state that it's read-only or note any side effects. However, the name 'get' implies retrieval, and it does explain what the returned JSON contains.

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 primary purpose and then detailing the output contents. Every sentence adds value without unnecessary fluff.

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 no output schema, the description adequately explains what the returned JSON includes (file index, dependency graph, architecture layers, etc.). It also notes the prerequisite of a completed analysis, making it sufficiently complete for an agent.

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 schema description covers the single parameter (job_id) fully with 'The job ID from analyze_repo.' The tool description adds no additional semantic meaning beyond that, but since schema coverage is 100%, the baseline of 3 is appropriate.

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 gets the full agent context JSON for a completed analysis, with a specific verb ('get') and resource ('full agent context JSON'). It distinguishes itself from sibling tools like get_summary and get_section by emphasizing it returns the complete structured context rather than a part.

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 clear context for when to use this tool: for a completed analysis and for persistent codebase understanding. It implies this is the comprehensive option compared to smaller retrievals, but doesn't explicitly name alternatives or when not to use it.

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