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Chaos-Intelligence-Inc

@chaosintelligence/mcp

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search_similar

Find semantically similar thoughts to a text query via AI embeddings. Adjust result count and minimum similarity score to refine matches.

Instructions

Find thoughts semantically similar to a text query using AI embeddings. Great for finding related ideas and concepts.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYesText to find similar thoughts for
limitNoMaximum number of results (default: 10, max: 50)
min_scoreNoMinimum similarity score 0-1 (default: 0.5)
Behavior2/5

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

No annotations are provided, so the description must disclose safety and behavior. It mentions the use of AI embeddings, but does not describe output format, ranking, result thresholds, or any side effects. As a read operation, it's safe, but the description lacks substantive behavioral context beyond the purpose.

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?

Two sentences, immediately front-loaded with the tool's purpose, with the second sentence adding usage context. No redundant words.

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

Completeness3/5

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

With full schema coverage and a simple read operation, the description is adequate but missing details about return output and any limitations. The absence of an output schema increases the burden, which is not fully met.

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?

All three parameters are fully described in the schema, so the description adds no additional parameter meaning. It earns the baseline of 3 given the complete 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 finds thoughts semantically similar to a text query using AI embeddings, which is specific and distinct from keyword search. However, it does not explicitly name alternative tools like search_thoughts for comparison, so it doesn't fully distinguish from siblings.

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

Usage Guidelines3/5

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

The description provides context for when to use it ('Great for finding related ideas and concepts'), but does not mention when not to use it, nor does it reference alternative sibling tools. The usage is implied rather than explicit.

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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