Returns Mastra (Bun) and LangGraph (Python) patterns for AI agent workflows.
Call this BEFORE create_workflow / update_draft when building chatbots, tool-using agents, or multi-step LLM flows. Do not hand-roll custom agent loops — use the preinstalled frameworks.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the burden of behavior. It clearly states the tool returns documentation/patterns, implying a read-only information lookup. It does not explicitly state no side effects or describe output format, but for a zero-parameter docs tool this is a minor gap.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Three compact, purposeful sentences: purpose first, then when to call it, then an anti-pattern warning. No wasted words and the key usage is front-loaded.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter docs lookup with no output schema, the description fully covers what it returns, when to invoke it, and what to avoid. Nothing an agent needs to select or call this tool correctly is missing.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool takes zero parameters, so there are no parameter semantics to clarify. This falls into the baseline-4 case where an empty input schema is sufficient and no further description is required.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb and resource: returns Mastra (Bun) and LangGraph (Python) patterns for AI agent workflows. It also names the sibling tools it feeds into, distinguishing it from other docs or workflow tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Call this BEFORE create_workflow / update_draft' and gives concrete use cases: chatbots, tool-using agents, multi-step LLM flows. It also provides a negative guideline against hand-rolling custom agent loops.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Every tool has a clearly distinct purpose: workflow lifecycle, invocation, logging, metrics, KV store, secrets, connections, and documentation. Even similar tools like get_logs vs get_invocation are well-differentiated by descriptions.
Most tools follow a consistent verb_noun pattern (create_workflow, get_workflow, list_workflows). The KV tools (kv_get, kv_set, kv_list, kv_delete) are internally consistent but deviate from the dominant verb_noun style by using a noun_verb prefix.
With 18 tools, the count is slightly above the typical 3-15 range, but the broad platform scope (workflow management, invocation, logging, metrics, KV, secrets, connections, docs) justifies each tool's existence. No tool feels redundant.
Workflows have create, read, update (via update_draft), list, and publish, but lack a delete operation. Secrets support create and list but no delete/update, and connections only have list. These lifecycle gaps create potential dead ends for agents.
