get_developer_context

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

The description adds value beyond the readOnlyHint annotation by specifying the exact data categories returned and confirming no parameters are needed. This gives the agent a clear understanding of behavior and scope.

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-loading the main purpose and clearly stating the parameter requirement. Every sentence adds value with no wasted 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?

Given the lack of parameters, output schema, and rich annotations, the description adequately covers what the tool does and returns. Lacking explicit output structure, but the listed data categories provide sufficient context for an agent to use the tool 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?

With no parameters defined in the schema (100% coverage), the description adds context by noting the API key usage. This provides meaning beyond the empty schema, earning a baseline of 4 for informative value.

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 identifies the tool's purpose: retrieving the authenticated user's developer profile. It lists specific data categories (coding statistics, subscription status, GitHub integration, organization memberships) which differentiates it from siblings like get_ai_insights or get_productivity_summary.

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 states no parameters are required and the tool uses the configured API key, providing clear context for when to invoke it. However, it does not explicitly mention when not to use it or compare to sibling tools, though the context of sibling names implies differentiation.

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