context_loop

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 effectively describes the tool's behavior: it 'runs ALL context health checks,' 'auto-extracts facts,' 'detects contradictions,' 'checks answer quality,' and 'tells you exactly what to do next.' It also details the return structure ('directive' object with specific fields) and provides a minimal call example. However, it lacks information on potential side effects, error handling, or performance characteristics like rate limits.

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 appropriately front-loaded with critical usage instructions, but it is verbose with some redundancy (e.g., repeating the tool's importance). Sentences like 'It is the single most important tool' and 'ESSENTIAL for:' could be more concise. While most content is valuable, the structure could be tighter to improve readability without losing essential information.

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 (8 parameters, no annotations, no output schema), the description is fairly complete. It explains the tool's purpose, usage, behavior, and return structure in detail. However, it lacks an output schema, so the description must fully describe return values, which it does with the 'directive' object fields. Gaps include no error handling details and limited parameter semantics, but overall, it provides sufficient context for effective use.

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 description coverage is high (88%), so the baseline is 3. The description adds minimal parameter semantics beyond the schema: it mentions 'most fields have smart defaults' and provides a minimal call example for 'messages.' However, it does not explain the purpose or interaction of parameters like 'sessionId,' 'claim,' or 'discoveryQuery,' nor does it clarify how parameters like 'currentInput' are 'auto-inferred.' The description compensates somewhat but not significantly.

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 ('runs ALL context health checks in one call') and resources ('auto-extracts facts from conversation, detects contradictions, checks answer quality'). It explicitly distinguishes this tool from its siblings by stating it 'replaces calling recap, conflict, ambiguity, entropy, grounding, drift, depth, and discovery tools individually,' making the 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 usage guidelines: 'CALL THIS TOOL every 2-3 turns and at the start of ANY task' and 'ESSENTIAL for: research tasks, multi-step workflows, long conversations, preserving context across turns, knowledge management, and any task requiring memory or fact-checking.' It also implicitly suggests when not to use it (for simpler tasks not requiring these features) and positions it as a replacement for multiple sibling tools, offering clear alternatives.

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