Knowledge Assistant MCP Server

query_knowledge_base

Ask questions to get answers from your documents via a multi-agent RAG pipeline. Receive a proposed answer for review and approval, enabling interactive refinement.

Instructions

Ask the knowledge assistant a question. Runs a multi-agent pipeline: coordinator -> retriever (RAG) -> synthesizer. Returns a proposed answer for your review. After reviewing, call approve_or_edit_answer to approve or request edits (human-in-the-loop).

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": "query_knowledge_base"
  }
}

Input Schema

Name	Required	Description	Default
`query`	Yes
`top_k`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 9, 2026

TDQS

A4.3/5.0

Behavior5/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It goes beyond a simple query by revealing the multi-agent orchestration, the fact that the answer is only a proposal, and the human-in-the-loop requirement. This gives the agent a clear understanding of the tool's non-final and collaborative nature.

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 three sentences long, each serving a distinct purpose: stating the action, explaining the internal pipeline, and providing the next-step workflow. It is front-loaded with the primary purpose and contains no redundant or irrelevant information.

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

Completeness4/5

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

The description is largely complete for a query tool: it covers the action, the pipeline, and the follow-up workflow. The existence of an output schema means return values need not be detailed. However, the missing parameter semantics for 'top_k' and the lack of explicit contrast with search_knowledge_base leave minor gaps in overall completeness.

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

Parameters2/5

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 for parameter meaning. The 'query' parameter is implicitly explained as the question to ask, but 'top_k' is not mentioned at all, leaving its role (e.g., number of retrieved documents) ambiguous. This is a significant gap given the lack of schema 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 uses a specific verb ('Ask') and resource ('knowledge assistant'), and clearly explains the multi-agent pipeline (coordinator -> retriever -> synthesizer). It distinguishes the tool from siblings by mentioning it returns a proposed answer for review, which is unlike a direct search or an approval tool.

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 workflow context by instructing the user to call approve_or_edit_answer after reviewing, which implicitly indicates a sequential use. However, it does not explicitly differentiate when to use this tool versus search_knowledge_base, missing the 'when-not' guidance.

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

Deploy Server