Knowledge Assistant MCP Server

approve_or_edit_answer

Approve a proposed answer from your knowledge base or provide feedback to request edits.

Instructions

Human-in-the-loop: approve the proposed answer from query_knowledge_base, or request edits. Set approved=True to accept, or approved=False and provide user_feedback for changes.

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": {
      "approved": "<approved>",
      "proposal_answer": "<proposal_answer>"
    },
    "name": "approve_or_edit_answer"
  }
}

Input Schema

Name	Required	Description	Default
`approved`	Yes
`user_feedback`	No
`proposal_answer`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Aug 9, 2026

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations provided, the description carries the full burden. It explains the decision logic (approved=True/False) and that user_feedback is needed for edits, which is useful. However, it does not disclose side effects, whether approval modifies the knowledge base, or any permissions/irreversibility. The tool's outcome beyond the parameters is left implicit.

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 no filler. It front-loads the core purpose ('Human-in-the-loop') and immediately provides the usage pattern. Every phrase contributes to understanding the tool's function.

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?

Given the tool's moderate complexity (3 params, 2 required) and the existence of an output schema, the description is adequate but not rich. It covers the branching logic and references the source (query_knowledge_base), but omits preconditions, post-conditions, or what happens next in the pipeline. This leaves room for agent confusion about workflow integration.

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?

Schema description coverage is 0%, so the description must compensate. It explains 'approved' (accept or request changes) and 'user_feedback' (changes to request), but 'proposal_answer' is only indirectly referenced as 'the proposed answer'. While this adds meaning beyond the empty schema, one parameter remains underspecified.

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 tool's purpose: to approve or request edits for an answer proposed by query_knowledge_base. It uses a specific verb ('approve', 'request edits') and identifies the resource ('the proposed answer'), distinguishing it from sibling tools like query_knowledge_base, add_documents, and search_knowledge_base.

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 implies the use case: after obtaining a proposal from query_knowledge_base, this tool decides on acceptance or revision. It gives clear context ('Human-in-the-loop') and ties the tool to a parent process, but does not explicitly name alternatives or state when not to use it.

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

Deploy Server