SQLite Project Memory MCP

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 'quick AI context recovery,' which implies a read-only operation for efficiency, but fails to detail critical aspects such as permissions, rate limits, data freshness, or what 'reasoning excerpts' entail. This leaves significant gaps for a tool with no annotation coverage.

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 is front-loaded with the core purpose. Every word contributes to understanding the tool's function without any redundancy or unnecessary elaboration.

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 moderate complexity (3 parameters, no annotations) and the presence of an output schema (which reduces the need to describe return values), the description is minimally adequate. However, it lacks details on parameters, behavioral traits, and usage context, making it incomplete for optimal agent understanding.

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 input schema has 3 parameters with 0% description coverage, and the tool description provides no information about them. It doesn't explain what 'limit', 'offset', or 'compact' mean in the context of returning reasoning excerpts, failing to compensate for the schema's lack of documentation.

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 a specific verb ('Return') and resource ('recent reasoning excerpts'), and provides a functional context ('for quick AI context recovery'). However, it doesn't explicitly differentiate from sibling tools like 'get_recent_activity' or 'get_decision_log' that might also retrieve recent information, which prevents a perfect score.

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. It doesn't mention sibling tools like 'get_recent_activity' or 'get_decision_log' that might serve similar purposes, nor does it specify prerequisites, exclusions, or optimal scenarios for usage.

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