cachly-mcp-server

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 states the tool 'Returns all global lessons or those matching a topic filter,' which covers basic output behavior but lacks details on permissions, rate limits, error handling, or data format. For a tool with zero annotation coverage, this is insufficient to fully inform an agent about its operational traits.

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 concise and front-loaded, consisting of two clear sentences that directly state the tool's purpose and behavior. There is no wasted language, and every sentence contributes essential information, making it efficient and well-structured.

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 (2 parameters, no output schema, no annotations), the description is adequate but incomplete. It covers the basic purpose and parameter usage but lacks details on return values, error conditions, or behavioral nuances. Without annotations or an output schema, the agent may struggle with full contextual understanding, though the description provides a minimum viable foundation.

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 input schema already documents both parameters ('instance_id' and 'topic') with descriptions. The description adds marginal value by mentioning 'topic filter' as optional, but doesn't provide additional semantics beyond what the schema specifies. This meets the baseline for high schema coverage.

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: 'Recall cross-project lessons stored via global_learn.' It specifies the verb ('recall'), resource ('cross-project lessons'), and source ('stored via global_learn'). However, it doesn't explicitly differentiate from sibling tools like 'smart_recall' or 'team_recall,' 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 implies usage by mentioning 'Returns all global lessons or those matching a topic filter,' suggesting it's for retrieving stored lessons with optional filtering. However, it lacks explicit guidance on when to use this tool versus alternatives like 'smart_recall' or 'team_recall,' and doesn't specify prerequisites or exclusions, leaving usage context somewhat vague.

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