Galatic.ai

Ask Flow assistant

ask_flow_assistant

Ask the Flow Studio assistant a question and get a text answer as { response }. With flow (a flow record from get_flow), the assistant is told only its node types, edge count, template type and execution mode, not node settings or prompts. It changes nothing: apply its advice with save_flow. Spends chat credits and needs a plan that includes chat.

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": {
      "message": "<message>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "ask_flow_assistant"
  }
}

Input Schema

Name	Required	Description
`flow`	No	A flow record from get_flow, for context.
`context`	Yes	Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
`message`	Yes	The question.
`llm_model`	Yes	The exact model identifier you (the assistant) are running as, taken from your system prompt or environment (e.g. "claude-opus-4-8", "gpt-5.2"). Used for analytics only. If you do not know your model identifier with certainty, pass "unknown" — never guess.
`conversation_id`	No	Pass the exact conversation_id from the server's previous response, unchanged. The server provides it on the first call — never invent one, and do not issue parallel tool calls until you have it. Keep passing the same conversation_id for the rest of the conversation, including after later user messages or on a different task; do not reset it when the user starts a new request.

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 27, 2026
- addedInput schema / properties / flow / description
```
Added value: +"A flow record from get_flow, for context."
```
- addedInput schema / properties / message / description
```
Added value: +"The question."
```
First observedOct 24, 2026

TDQS

A4.4/5.0

Behavior4/5

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

Adds real context beyond the annotations: it discloses the cost model ('Spends chat credits'), a plan prerequisite ('needs a plan that includes chat'), and exactly what flow data is transmitted to the assistant versus withheld. readOnlyHint=false is consistent with the credit-spending side effect rather than a contradiction of 'changes nothing'.

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 dense sentences, front-loaded with purpose, then the context limitation, then the safety/cost profile. No filler and every clause carries actionable 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?

Covers side effects, prerequisites, and the follow-up mutation path, which is the critical information for what is effectively an advisory wrapper. The conversation_id continuity requirement is left entirely to the schema, which is acceptable given its rich description, but the description itself does not surface it.

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

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description earns above baseline by explaining what the 'flow' parameter actually transmits (node types, edge count, template type, execution mode — not node settings or prompts), which meaningfully shapes how the agent should use that parameter.

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?

States a specific verb and resource ('Ask the Flow Studio assistant a question') and names the exact return shape ('a text answer as { response }'). It is clearly distinct from sibling tools like get_prompt_suggestions or improve_prompt, which act on prompts rather than answering questions about a flow.

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?

Explicitly positions the tool as advisory ('It changes nothing: apply its advice with save_flow'), which tells the agent to follow up with save_flow for mutations. It does not explicitly compare against siblings such as improve_prompt or get_prompt_suggestions, so the alternatives guidance is incomplete.

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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