Distill a user's dialogue into an intention wavefunction (explicit intentions + confidence). dialogue: [{role, content}, ...].
| Name | Required | Description | Default |
|---|---|---|---|
| user_id | Yes | ||
| dialogue | Yes |
| Name | Required | Description | Default |
|---|---|---|---|
| result | Yes |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavioral traits. It mentions the processing of dialogue into intentions but does not specify side effects, authorization needs, or data persistence. This lack of detail leaves the agent without essential operational context.
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, front-loaded sentence. It is concise and avoids redundancy, though it uses jargon ('intention wavefunction') that may reduce clarity. Nonetheless, it efficiently communicates the core function.
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 (processing dialogue into structured output), the description is insufficient. It does not explain the output format despite the presence of an output schema. Key aspects like the meaning of 'confidence' or the structure of 'intention wavefunction' are missing. The agent would need additional context to use the tool effectively.
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 0%, so the description must compensate. It partially explains the 'dialogue' parameter format but omits the 'user_id' parameter entirely. The explanation of 'dialogue' as '[role, content]' maps to the schema, but lacks depth (e.g., allowed roles or content types). Insufficient for an agent to correctly construct the arguments.
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: to distill user dialogue into an intention wavefunction with explicit intentions and confidence. The verb 'distill' is specific and the resource is well-defined. The output concept is unique among siblings like collapse or describe_agent, making it distinct.
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?
No explicit guidance on when to use this tool versus siblings. The description does not provide contextual cues, prerequisites, or exclusions. It merely states the function without any usage recommendations.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool has a distinct role in the workflow: intake captures user input, collapse finalizes commitment, find_matches searches the index, register_collective adds to the index, and describe_agent returns metadata. No overlap in purpose.
All tool names use imperative verbs with consistent snake_case for compound names (describe_agent, find_matches, register_collective). Simple names collapse and intake follow the same pattern.
Five tools appropriately cover the core workflow of intention intake, commitment, matching, registration, and self-description. The count is well-scoped for a specialized search server.
The tool set covers the primary use case, but lacks update/deregister operations for collectives. However, these are not essential for the search-focused purpose, so only a minor gap.