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FlowCastle

by ai.flowcastle
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get_flow_example

Read-only

Return one reusable flow example by id, optionally with a complete action batch you can adapt and pass to apply_actions. Read-only, needs no API key. Call search_flow_examples first to find the id.

Input Schema

NameRequiredDescriptionDefault
idYesRequired. Example id exactly as returned by search_flow_examples.
localeNoLanguage of the bot texts inside the action batch, e.g. "ru", when the example has curated texts in it. Defaults to English. The guidance fields are always English.
includeSchemaExampleNoSet true to include the full action-batch example — much larger, but it is the part you adapt for apply_actions. Defaults to false.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / locale
      Added value: +{
+  "description": "Language of the bot texts inside the action batch, e.g. \"ru\", when the example has curated texts in it. Defaults to English. The guidance fields are always English.",
+  "type": "string"
+}
  2. Changed2 schema fields changed
    • addedInput schema / properties / id / description
      Added value: +"Required. Example id exactly as returned by search_flow_examples."
    • addedInput schema / properties / includeSchemaExample / description
      Added value: +"Set true to include the full action-batch example — much larger, but it is the part you adapt for apply_actions. Defaults to false."
  3. First observed

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover readOnly/destructive/openWorld, but the description adds genuinely new context: no API key required, and the size trade-off of includeSchemaExample ('much larger'). That is useful behavioral disclosure beyond the structured fields.

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?

Two sentences, zero waste, with the resource and scope front-loaded and the prerequisite/consumer constraints following. Every clause 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?

With no output schema, the description still tells the agent what comes back (example, optionally a full action batch) and how to obtain the id. Minor gaps remain around return shape, but it is sufficient to call the tool correctly.

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 parameters are documented; the description still adds workflow meaning by framing the action batch as the artifact you adapt for apply_actions. It doesn't add syntax beyond the schema, so not a 5.

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 ('Return one reusable flow example by id') and clarifies the optional action-batch variant. An agent can immediately distinguish it from search_flow_examples, which is named as the lookup counterpart.

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?

Explicit prerequisite ('Call search_flow_examples first to find the id') and downstream consumer ('pass to apply_actions') give clear routing. There is no explicit when-not-to-use condition, which keeps it just below the top score.

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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