Galatic.ai

Flow run

get_flow_run

Read-onlyIdempotent

Get one Flow Studio run: status ("running", "paused", "failed", "budget_exceeded", "cancelled", ...), nodeStatuses keyed by node id (a node waiting for a person reads "awaiting_approval"), billedCredits (what the account is charged), budgetCap, timestamps and the published output URL. Poll it after run_flow or a rerun; act on it with control_flow_run. Read-only; 404 for another user's run.

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": {
      "flowRunId": "<flowRunId>",
      "context": "<context>",
      "llm_model": "<llm_model>"
    },
    "name": "get_flow_run"
  }
}

Input Schema

Name	Required	Description
`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."
`flowRunId`	Yes	From run_flow, or newFlowRunId from control_flow_run rerun.
`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.

Changed1 schema field changedOct 27, 2026
- addedInput schema / properties / flowRunId / description
```
Added value: +"From run_flow, or newFlowRunId from control_flow_run rerun."
```
First observedOct 24, 2026

TDQS

A4.6/5.0

Behavior5/5

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

Annotations already establish read-only, idempotent, non-destructive behavior, but the description adds meaningful context beyond them: the specific status vocabulary, that a waiting node reads 'awaiting_approval', that billedCredits is what the account is charged, and that another user's run returns 404. These details help the agent interpret responses and anticipate access limitations.

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 front-loaded with the core purpose and returned fields, then closes with concise usage and access notes. Every sentence earns its place: fields, polling guidance, sibling alternative, and 404 behavior.

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?

There is no output schema, so the description carries the burden of explaining return values, and it does so by enumerating the key fields and status meanings. It also covers usage context, the related mutating tool, and ownership behavior, making it complete enough for an agent to call and interpret the result correctly.

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

Parameters2/5

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

With 100% schema description coverage, the schema already documents flowRunId, context, llm_model, and conversation_id thoroughly. The description does not add parameter-level meaning beyond the schema; it only implies polling and rerun sourcing, which the schema also covers. Baseline would be 3, but the description contributes essentially no additional parameter semantics.

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 names a specific verb and resource ('Get one Flow Studio run') and enumerates the exact fields returned: status values, nodeStatuses keyed by node id, billedCredits, budgetCap, timestamps, and output URL. It clearly distinguishes itself from control_flow_run by positioning that sibling as the mutating tool.

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?

It gives explicit usage direction: 'Poll it after run_flow or a rerun; act on it with control_flow_run.' That tells the agent both when to call this tool and which alternative to use for actions. It also states the 404 behavior for another user's run, which is a useful boundary.

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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