get_narrative_context

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions aggregation but doesn't describe how the context is compiled (e.g., from what sources), whether it includes real-time updates, any rate limits, or what the output format looks like. For a tool with 5 parameters and no output schema, this leaves significant gaps in understanding its behavior.

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 a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Aggregates comprehensive narrative context... for the LLM system prompt') directly contributes to understanding the tool's function, making it highly concise and well-structured.

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's complexity (5 parameters, no annotations, no output schema) and the description's brevity, it is minimally adequate. The purpose is clear, but without behavioral details, output information, or usage guidelines, it relies heavily on the schema for parameter understanding. This leaves gaps for an AI agent to fully leverage the tool effectively.

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 80%, with 4 out of 5 parameters having descriptions. The tool description does not add any parameter-specific information beyond what the schema provides (e.g., it doesn't explain relationships between worldId, characterId, and encounterId). Given the high schema coverage, the baseline score of 3 is appropriate as the description doesn't compensate but also doesn't detract.

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: 'Aggregates comprehensive narrative context (Character, World, Combat, Secrets) for the LLM system prompt.' It specifies the verb ('aggregates') and resource ('narrative context') with categories. However, it doesn't explicitly distinguish this tool from similar siblings like get_narrative_context_notes or get_party_context, which might also provide contextual information.

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 no guidance on when to use this tool versus alternatives. With many sibling tools that retrieve context (e.g., get_narrative_context_notes, get_party_context, get_world_state), there is no indication of when this comprehensive aggregation is preferred over more specific tools. Usage is implied by the purpose but not explicitly stated.

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