pasm_context

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

With no annotations provided, the description carries the behavioral disclosure burden. It explicitly states the operation is read-only and does not write memory, and it explains the intended downstream behavior of treating recalled content as 'things you actually remember'. This is meaningful transparency for a context-retrieval tool.

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 compact and well-structured: it front-loads the read-only nature and return payload, then provides a clear usage directive. Every sentence earns its place and there is no redundant prose.

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 tool with no output schema and no annotations, the description adequately covers the return contents and how to use them in a prompt. It could mention edge cases or output formatting, but the provided information is sufficient for correct invocation.

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 baseline is 3. The description adds no new parameter-level detail beyond referencing the current topic; parameter semantics are already fully handled by the input schema.

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 ('取', get) with a clear resource ('PASM 认知上下文') and states it is read-only and does not write memory. It also lists the return payload, which distinguishes it from memory-writing sibling tools like pasm_save and pasm_feedback.

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 this is the preferred tool for integrating PASM into an LLM prompt and to call it first. It gives clear context for when to use it, but it does not mention when not to use it or name alternative sibling tools for exclusion.

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