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@asyntai/mcp

@asyntai/mcp

Official
by asyntai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
JavaScript
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Search the knowledge base

search_knowledge_base
Read-onlyIdempotent

Search a website's knowledge base to retrieve passages and relevance scores, showing what the chatbot knows about a topic without generating an answer.

Instructions

Search one website's knowledge base and return the passages the chatbot would retrieve for this question, with a relevance score. Use this to check what the chatbot knows about a topic. It does not generate an answer and does not use the message allowance.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search_knowledge_base"
  }
}

Input Schema

NameRequiredDescriptionDefault
limitNoHow many passages to return (1-20, default 5).
queryYesWhat to look for, in plain language.
website_idNoNumeric id of the website, from list_websites. Omit to use the account's primary website.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.2

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds valuable context beyond these: it returns passages with relevance scores, and notably does not consume the message allowance—a behavioral trait not captured by annotations. This additional context helps the agent understand side effects and costs. It could mention pagination or error behavior, but the annotations lower the bar, so a 4 is appropriate.

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 two sentences with zero filler. It leads with the core action and output, then states the use case and key exclusions. Every sentence earns its place, and the most decision-relevant information (what it returns and what it doesn't do) is front-loaded.

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?

For a read-only search tool with no output schema, the description sufficiently explains the return value (passages with relevance scores) and explicitly notes that it does not use the message allowance—a cost-related detail agents need. The safety profile is already covered by annotations, and parameter details are fully in the schema. Nothing essential for invoking the tool correctly is missing.

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?

The input schema provides 100% description coverage for all three parameters (query, limit, website_id), including defaults and semantics. The description adds no parameter-specific information beyond what the schema already provides, so the baseline of 3 applies. The description's mention of 'for this question' loosely aligns with the query parameter but adds no new syntax or format details.

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 the action: 'Search one website's knowledge base' and specifies the output: 'return the passages the chatbot would retrieve for this question, with a relevance score.' It also differentiates from siblings like ask_ai by noting it does not generate an answer, and from search_asyntai_docs by targeting the knowledge base specifically. The verb-resource pair is distinct and unambiguous.

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 a clear intended use case: 'Use this to check what the chatbot knows about a topic.' It also explicitly states what it does not do ('does not generate an answer'), which guides the agent away from using it for answer generation. However, it does not name alternative tools (e.g., search_asyntai_docs) or specify when to prefer them, leaving some inference to the agent.

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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