Get API endpoints, base URL, and a working code example for a specific integration provider. Call this before writing workflow code that uses an integration.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | Yes | Provider slug, e.g. github, salesforce, slack, hubspot, notion, linear, stripe, zendesk, airtable |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the behavioral disclosure burden. It clearly indicates the tool returns documentation content (endpoints, base URL, code example) and implies a read-only retrieval operation through 'Get' and 'Call this before...'. However, it does not disclose error behavior, prerequisites like existing connections, or whether any configuration is returned beyond the listed items.
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?
Two short sentences, no filler. The first sentence states the purpose and output, and the second provides actionable usage guidance. All content earns its place.
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 single-parameter tool with no output schema, the description covers the key contextual needs: what the tool returns and when to call it. It does not detail the exact response shape, but the listed items (endpoints, base URL, code example) give sufficient expectation. This is adequate for an agent to select and invoke the tool correctly.
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?
Schema description coverage is 100% for the single 'provider' parameter, which already includes a clear description and concrete examples. The description adds only that the provider is 'specific', but does not need to add more because the schema fully documents the parameter format and valid values.
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?
The description states a specific verb ('Get') and resource ('API endpoints, base URL, and a working code example for a specific integration provider'), making the tool's purpose immediately clear. It also distinguishes itself from sibling tools like get_agent_framework_docs by focusing on integration providers rather than agent frameworks.
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?
The description gives explicit guidance: 'Call this before writing workflow code that uses an integration.' This clearly establishes when to use the tool. It does not mention alternatives or when not to use it, but for a simple documentation-fetching tool the timing guidance is strong and practical.
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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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.
