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E-Commerce Support Agent MCP Server

E-Commerce Support Agent MCP Server

by CuriousMonkey414
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

search_policies

Find relevant policy information by searching company policy documents. Retrieve matching sections with source, content, and relevance scores to answer customer questions.

Instructions

Search company policy documents for text relevant to a question.

Args: query: A self-contained search query. Conversation history is not available on this surface, so include whatever context the search needs directly in the query text.

Returns: Matching policy chunks (source, section, content, relevance score) when found; grounded=False when nothing relevant enough was retrieved.

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_policies"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explicitly describes the return value ('matching policy chunks') and the fallback behavior ('grounded=False when nothing relevant enough was retrieved'). This adds useful transparency beyond what a search tool would infer.

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 concise and well-organized into Args and Returns sections. Every sentence adds value, and there is no redundant or vague phrasing. It is a model of efficient documentation.

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 single parameter and existing output schema, the description is complete. It explains the purpose, the query construction, and the return behavior, including the grounded flag. No critical context is missing.

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 input schema only defines 'query' as a string, but the description compensates with detailed guidance: 'A self-contained search query. Conversation history is not available on this surface, so include whatever context the search needs directly in the query text.' This adds significant semantic meaning beyond the schema, so the parameter is fully explained.

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: 'Search company policy documents for text relevant to a question.' It uses a specific verb (search) and resource (policy documents) and distinguishes from sibling tools that handle customer orders and accounts.

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 usage context: it tells the agent to craft a self-contained query because conversation history is unavailable. While it doesn't explicitly name alternatives, the purpose and sibling list imply this is for policy searches only, so this counts as clear context without exclusions.

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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