Meet Rupert MCP Server

search_documents

Find documents in the Meet Rupert knowledgebase by title substring and retrieve metadata (ID, title, draft status, category, last-updated). Use the returned ID with read_document to fetch full content.

Instructions

Search the Meet Rupert knowledgebase for documents by title. Returns a paginated list of matching documents with their UUIDs, titles, draft status, categories and last-updated dates. Use the returned id with read_document to fetch full content. This is a title/metadata search — to ask a natural-language question answered from document content, use ask_ai instead.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "search_documents"
  }
}

Input Schema

Name	Required	Description
`page`	No	Page number, starting at 1 (default 1).
`query`	No	Substring to match against document titles. Omit to list all documents.
`pageSize`	No	Results per page, 1–100 (default 20).

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Aug 8, 2026

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the return fields (UUIDs, titles, draft status, categories, last-updated dates), pagination, and the fact that it matches on title/metadata, not content. However, it omits potential details like case sensitivity, ordering, or inclusion of drafts, which are not fully disclosed.

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?

Three concise sentences deliver purpose, return details, and usage guidance without redundancy. The most critical information (title-based search) is front-loaded, and every sentence earns its place.

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?

Without an output schema, the description enumerates return fields and links to read_document, which aids programmatic use. It lacks details about the response envelope (e.g., total counts, nested structure), but given the moderate complexity and strong chaining hints, it is reasonably complete.

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 coverage is 100% for all three parameters, each with clear descriptions. The tool description adds context about pagination and linking to read_document, but does not meaningfully enhance the parameter semantics beyond what the schema already documents, so baseline 3 is appropriate.

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 searches the Meet Rupert knowledgebase by title, with specific verb and resource. It also immediately distinguishes itself from ask_ai, which queries document content, preventing confusion with sibling tools.

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

Usage Guidelines5/5

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

Explicit guidance is provided: it states this is a title/metadata search, and directs agents to use ask_ai for natural-language content queries. It also recommends using the returned id with read_document, suggesting a clear workflow and alternative.

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

Deploy Server