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vault_search

Search belief artifacts with full-text TF-IDF ranking to retrieve top relevant matches and excerpts.

Instructions

Full-text search across all belief artifacts in the vault.

Uses TF-IDF ranking with entity-name boosting (3x) to find the most relevant beliefs. Much cheaper than listing all beliefs — returns only the top matches with excerpts.

Args: query: Natural language search query (e.g., "how does knapsack work?") top_k: Maximum number of results to return (default: 5)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
top_kNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the full burden. It discloses that the tool uses TF-IDF with entity-name boosting, is cheap, and returns excerpts. It does not mention whether it is read-only or any authentication needs, but the default is a read operation.

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?

The description is extremely concise: a single sentence for the purpose, then a line about efficiency, followed by inline parameter docs. Every sentence adds value with no redundancy.

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

Completeness5/5

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

Given the presence of an output schema (which explains return values), the description covers purpose, usage context, cost comparison, and parameter details. It is complete for a moderate-complexity search tool.

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

Parameters5/5

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

The schema has 0% description coverage, so the description's explicit Args section adds significant meaning: query is described as 'natural language search query' with an example, and top_k is explained as 'maximum number of results' with a default. This fully compensates for the schema's lack of descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states 'Full-text search across all belief artifacts in the vault', using a specific verb ('search') and resource ('belief artifacts'). It distinguishes itself from sibling tools like vault_query by specifying TF-IDF ranking and entity-name boosting.

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

Usage Guidelines4/5

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

The description provides clear context for when to use this tool: it is 'much cheaper than listing all beliefs' and returns only top matches. However, it does not explicitly state when not to use it or mention alternatives beyond this contrast.

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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