init_session

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

No annotations are present, so the description carries the full behavioral burden. It discloses auto-discovery from the API token, the side effect of auto-selecting team/project for subsequent calls, and the returned payload (AI task queue). It does not cover idempotency or failure modes (e.g., no token, zero teams/projects), but the state-changing behavior is well documented.

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 front-loaded with purpose and ordering ('Initialize the MCP session. Call this FIRST'), and each sentence contributes distinct information. At five sentences it is slightly longer than minimal, but the length is justified because with 0 parameters and no annotations, the description must carry the setup semantics.

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?

For a 0-parameter tool with no output schema and no annotations, the description covers the essential call sequence (first), the return value (AI task queue), and the follow-up route (set_context). Only edge-case behavior such as authentication failure or an empty team/project list is missing, which is a minor gap for a session-init tool.

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 tool has 0 parameters, so the baseline is 4. The description adds meaningful context about implicit inputs — the API token and the resulting team/project auto-selection — which is the semantic information an agent needs in place of explicit parameters.

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 uses a specific verb-plus-resource ('Initialize the MCP session'), states the tool's key output ('returns the AI task queue'), and differentiates itself from siblings by positioning it as the mandatory first call, distinct from set_context, list_teams, and list_ai_queue. An agent can immediately understand what this tool does and why it exists.

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 says 'Call this FIRST before any other tool,' which is unambiguous when-to-use guidance. It also names the exact alternative and condition: 'If you have multiple teams or projects, call set_context afterward.' This is textbook explicit when/when-not/alternatives guidance.

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