ClickHelp Published Docs MCP Server

Ask AI Assistant

ask_ai

Read-only

Asks the portal's AI assistant a question and gets an answer based on the portal's documentation content

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": "ask_ai"
  }
}

Input Schema

Name	Required	Description
`query`	Yes	The question to ask the AI assistant
`chatId`	No	Optional chat ID (GUID) to continue an existing conversation
`projectId`	No	Optional project ID to restrict the answer context to a specific project. Specifying the ID is strongly recommended (if known) as it gives better context and improves answer precision. If the assistant fails to find answer in the specified project, it falls back to searching the entire portal for answer anyway.

Schema Changelog

Changes observed during successful MCP inspections.

First observedNov 11, 2026

TDQS

B3.4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds nothing beyond that, and notably omits that answers are non-deterministic (idempotentHint=false) and that answers can silently widen to the whole portal — behavior disclosed only in the projectId schema text.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with no filler or repetition. It is efficiently worded, though the space saved is not reinvested in any of the behavioral or routing detail the tool needs.

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?

For a simple three-parameter, read-only tool with a fully documented schema and no output schema, this is close to adequate. It still leaves the agent without a sense of what the answer looks like (length, citations, whether an answer may be 'not found in docs') or how it differs from a portal search.

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 100%, so the schema already explains query, chatId and projectId in detail, including the recommended-project and fallback behavior. The description adds no parameter meaning of its own; baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description names a specific verb and resource ('Asks the portal's AI assistant a question and gets an answer') and scopes the answer source to 'the portal's documentation content'. It is clear on its own, but it never contrasts itself with the retrieval siblings (search_portal, get_topic), which an agent must disambiguate between.

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

Usage Guidelines3/5

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

Usage is only implied: ask a natural-language question when a synthesized answer is wanted. There is no explicit when-to-use/when-not guidance and no forward reference to search_portal for cases where raw document hits are preferable to a generated answer.

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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