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@lstpsche/apidog-mcp

by lstpsche

apidog_import_openapi

Import OpenAPI specs into Apidog modules. Automatically batches large specs to avoid payload limits, supporting file paths or inline JSON.

Instructions

Import an OpenAPI spec into an Apidog module. Auto-batches large specs to avoid payload limits. Accepts a file path or inline JSON.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
specNoOpenAPI spec as JSON object
moduleYesModule name
projectNoProject name. Required when multiple projects are configured, optional otherwise.
specPathNoPath to OpenAPI spec JSON file
batchSizeNoPaths per import batch (default 15)
updateFoldersNoUpdate folder assignments from x-apidog-folder (default false)
deleteUnmatchedNoDelete endpoints not in the imported spec (default false)
overwriteBehaviorNoHow to handle matched endpoints (default OVERWRITE_EXISTING)
Behavior3/5

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

With no annotations provided, the description carries the burden of disclosing behavior. It mentions auto-batching to avoid payload limits and support for both file path and inline JSON. However, it does not mention potential side effects such as overwriting existing endpoints or deleting unmatched ones, which are important for a mutation tool. These are covered in the schema parameters, but the description itself is thin on side-effect transparency.

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 two sentences with no filler. It front-loads the primary action ('Import an OpenAPI spec') and then adds key operational details (batching, input formats). Every sentence 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?

Given the tool has 8 parameters, no output schema, and no annotations, the description is compact but the schema provides thorough parameter documentation. The description adds the essential operational context (module, input flexibility, batching). It could have summarized the overwrite/delete behavior, but the schema already documents those parameters, so the completeness is adequate.

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 adds value by clarifying that input can be a file path or inline JSON (spec vs specPath) and relates batchSize to the auto-batching behavior. This goes beyond the individual schema descriptions, which only state the field names and defaults.

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 clearly states the tool's primary function: 'Import an OpenAPI spec into an Apidog module.' It uses a specific verb ('import') and resource ('OpenAPI spec'), and distinguishes itself from the sibling export/list tools. The mention of auto-batching and input flexibility adds further specificity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies the tool should be used when importing an OpenAPI spec into an Apidog module, but it does not explicitly state when to use it over alternatives or provide exclusions. There are no references to other sibling tools, so the usage context is inferred rather than explicit.

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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