claude-session-continuity-mcp

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states 'Read-only' (clarifying safety), mentions AI-powered synthesis and contextual recommendation (behavioral traits), and references retrieval from an archive. However, it doesn't cover potential limitations like rate limits, authentication needs, or response format details.

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 the core purpose, followed by behavioral context and usage guidelines. Every sentence adds value: the first defines the tool, the second clarifies its read-only nature and differentiates from siblings. No wasted words, and the structure guides the agent efficiently.

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 no annotations and no output schema, the description does well by covering purpose, safety ('Read-only'), and sibling differentiation. However, it lacks details on return values (e.g., what the AI recommendation looks like) and doesn't mention error handling or prerequisites, leaving some gaps for a tool with AI synthesis.

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 100%, so the schema already documents both parameters fully. The description adds no additional parameter semantics beyond what's in the schema (e.g., no examples or edge cases). The baseline of 3 is appropriate when the schema handles parameter documentation effectively.

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 with specific verbs ('Get AI-powered fix suggestions', 'Retrieves', 'generates') and resources ('for a current error based on the solution archive'). It explicitly distinguishes from sibling tools solution_find and solution_record, making the scope and differentiation unambiguous.

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 explicit guidance on when to use this tool versus alternatives: 'Use solution_find for direct archive lookup without AI synthesis; use solution_record after fixing an error to grow the archive.' This clearly defines the tool's role in the workflow and distinguishes it from siblings.

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