Convolut 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 mentions AI involvement and the outcome ('consolidated summary or composition'), but lacks critical details: it doesn't specify if this is a read-only or destructive operation, what permissions are required, how long processing takes, or what the output format looks like. For a tool that merges data with AI, this gap is significant and limits predictability.

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 front-loads the core action ('Merge multiple contexts using AI') and outcome ('create a consolidated summary or composition'). There is no wasted verbiage or redundancy, making it easy to parse quickly while conveying 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 complexity of merging contexts with AI, lack of annotations, and no output schema, the description is incomplete. It doesn't address behavioral aspects like safety, performance, or output format, which are crucial for an AI-driven tool. While the schema covers parameters well, the overall context for reliable tool invocation is insufficient, especially compared to siblings that may have clearer purposes.

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%, providing detailed parameter documentation (e.g., 'context_ids' as UUIDs with min/max items, 'consolidation_type' with enum values). The description adds minimal value beyond the schema, only implying that 'consolidation_type' affects the output ('summary or composition'). It doesn't explain the difference between 'summarize' and 'compose' or how 'custom_prompt' influences the process, so it meets the baseline but doesn't enhance understanding.

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: 'Merge multiple contexts using AI to create a consolidated summary or composition.' It specifies the verb ('merge'), resource ('contexts'), and outcome ('consolidated summary or composition'), distinguishing it from siblings like 'list_contexts' or 'search_contexts' that don't involve merging. However, it doesn't explicitly differentiate from 'plan_from_contexts', which might have overlapping functionality, preventing 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 when to choose 'summarize' vs. 'compose', or how this differs from siblings like 'plan_from_contexts' or 'export_contexts'. Without such context, users must infer usage from the tool name and parameters alone, which is insufficient for effective tool selection.

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