Mockbird

import_data

Create a live mock API from existing artifacts. Auto-detects: OpenAPI 3.x / Swagger 2.0 spec (JSON or YAML) → resources with realistic seeded data; json-server db.json → hosts your exact records; Postman Collection v2.x → resources from requests, saved example responses become records verbatim; CSV/TSV → one typed collection (numbers/booleans inferred per column). Max 512 KB. Returns {id, adminKey, baseUrl, warnings[]}.

Input Schema

TableJSON Schema

Name	Required	Description
`name`	No	Project name override. Optional.
`seed`	No	Records to seed per resource for OpenAPI specs (default 20, max 100).
`content`	Yes	The raw spec / db.json / collection / CSV text.
`resource`	No	CSV only: collection name (default items).

TDQS

A4.1/5.0

Behavior4/5

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

No annotations exist, so the description carries full burden. It discloses auto-detection behavior, max file size (512 KB), and return object fields (id, adminKey, baseUrl, warnings). It also explains how Postman examples and CSV types are handled. This is solid behavioral context.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph, efficient and front-loaded with the main purpose. It covers all key points without wasted words. Could be slightly more structured but is effective.

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's complexity (multiple artifact types, auto-detection, parameters), the description is quite complete. No output schema exists, but return values are described. Sibling tools hint at missing guidance but overall the description provides sufficient context for an agent.

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 description coverage is 100%, so baseline is 3. The description adds meaning: explains 'content' accepts various artifact types, 'seed' default/max for OpenAPI, and 'resource' is CSV-only with default 'items'. This enhances parameter understanding.

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 purpose: 'Create a live mock API from existing artifacts.' It lists specific artifact types (OpenAPI, Swagger, json-server, Postman, CSV/TSV) and what each produces, distinguishing it from sibling tools like 'create_project' or 'add_resource'.

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 usage (when you have an existing artifact) but lacks explicit guidance on when not to use this tool or alternatives. No exclusions or comparisons to siblings are provided.

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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TDQS

A4.4/5.0

Disambiguation5/5

Each tool maps to a distinct resource/operation: project lifecycle, resource seeding, artifact import, record CRUD, custom routes, snapshots, request inspection, and external API status. Even the data-population tools are clearly separated by existing project (add_resource) versus new project (import_data/create_project).

Naming Consistency4/5

Most tools follow a clear verb_noun pattern (add_resource, create_project, query_records, write_record, delete_project, etc.). A few noun-style names (custom_route, project_info, snapshots) break the pattern, but all names are snake_case and readable.

Tool Count5/5

Eleven tools is a well-scoped set for a mock API server: project creation/deletion, data population/import, record operations, snapshots, custom routes, inspection, and API status all have dedicated tools without redundancy.

Completeness4/5

The core workflow is well covered: create/delete projects, add/import resources, CRUD records, snapshot state, define routes, and inspect requests. The main gaps are the lack of explicit resource deletion and custom-route update/delete, but these are workaroundable rather than blocking.

Resources

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