ctx_save

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

With no annotations provided, the description must carry the behavioral disclosure burden. It adds value by noting that context is "persistent" and lasts "across AI coding sessions," implying a long-term write operation. However, it does not mention whether saves overwrite existing entries, require special permissions, or produce specific side effects.

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 brief and front-loaded, with the core action in the first sentence. However, there is some redundancy: 'memory, decision, rule, or fact' overlaps with 'technical decisions, project rules, coding conventions', and 'persistent context' is repeated as 'persist across AI coding sessions'.

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?

The tool has 8 parameters, no schema descriptions, and no annotations, making it relatively complex. The description covers purpose and usage but leaves parameter semantics entirely unaddressed. An output schema exists, so return values are likely specified there, but the agent still lacks critical information about how to properly configure saves.

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 schema has 0% description coverage for its 8 parameters, and the description does not compensate. It mentions 'content' implicitly by listing types of content to save, but gives no guidance on how to use 'type', 'scope', 'tags', 'confidence', 'importance', 'source', or 'source_type'. The agent is left to infer parameter semantics from names and defaults alone.

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 explicitly states "Save a memory, decision, rule, or fact to persistent context," providing a specific verb (save) and resource (persistent context). It lists example content types, which clearly distinguishes this from read/search siblings like ctx_search and ctx_get.

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 second sentence gives clear usage context: "Use this to store technical decisions, project rules, coding conventions, or any important context that should persist across AI coding sessions." This tells the agent when to use the tool, though it does not explicitly name alternatives or when-not-to-use scenarios.

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