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initConversation

Start and organize AI conversations by storing user messages, generating banners, and retrieving context in a single operation with Cursor10x MCP.

Instructions

Initializes a conversation by storing the user message, generating a banner, and retrieving context in one operation

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
contentYesContent of the user message
importanceNoImportance level (low, medium, high)low
metadataNoOptional metadata for the message
Behavior2/5

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

With no annotations, the description carries full burden. It mentions behavioral outcomes (storing, generating, retrieving) but lacks critical details: whether this is a write operation (implied by 'storing'), permission requirements, rate limits, or what 'retrieving context' entails. The multi-operation nature suggests complexity that isn't fully disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence efficiently conveys the core functionality with zero wasted words. It's front-loaded with the main action ('initializes a conversation') followed by key operations.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a tool with no annotations, no output schema, and performing multiple operations (store, generate, retrieve), the description is insufficient. It doesn't explain the banner format, what context is retrieved, how results are returned, or error handling. The complexity warrants more disclosure.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema fully documents all 3 parameters. The description adds no parameter-specific information beyond implying 'content' is the user message. 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.

Purpose4/5

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 ('initializes', 'storing', 'generating', 'retrieving') and identifies the resource ('conversation'). It distinguishes from siblings like 'storeUserMessage' or 'generateBanner' by combining multiple operations, but doesn't explicitly contrast them.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No explicit guidance on when to use this tool versus alternatives like 'storeUserMessage' + 'generateBanner' + 'getComprehensiveContext'. The description implies it's for starting conversations, but lacks context on prerequisites, timing, or exclusions.

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

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