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Glama

Mockbird

by dev.workers.mockbird.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
NameRequiredDescriptionDefault
nameNoProject name override. Optional.
seedNoRecords to seed per resource for OpenAPI specs (default 20, max 100).
contentYesThe raw spec / db.json / collection / CSV text.
resourceNoCSV only: collection name (default items).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.5/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well. It discloses auto-detection behavior, per-format transformation rules, a size constraint, and the exact return shape including id, adminKey, baseUrl, and warnings.

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 dense but highly efficient. Every sentence conveys meaningful behavior: format mapping, size limit, and return structure. The primary action is front-loaded and the format list is easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity and lack of an output schema, the description is complete enough for an agent to call it correctly. It covers what inputs are accepted, how each is handled, the size limit, and what the response will contain.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds useful context about content auto-detection and seeded data, but it does not materially expand on parameter semantics beyond what the schema provides.

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 states a specific verb and resource: 'Create a live mock API from existing artifacts.' It enumerates distinct artifact types and their mappings, making it easy to distinguish from siblings like add_resource or create_project.

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?

The description clearly implies when to use this tool: when the user has an existing OpenAPI spec, db.json, Postman Collection, or CSV/TSV to convert into a mock API. It also provides a 512 KB limit, which is a useful exclusion criterion, though it does not explicitly name alternatives.

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

A4.2/5.0
Disambiguation5/5

Every tool targets a distinct resource or action: project creation, data seeding, record CRUD, traffic inspection, snapshots, and monitoring are all clearly separated. The four monitoring-related tools are carefully differentiated with cross-references, so an agent is unlikely to misselect.

Naming Consistency3/5

Most data and lifecycle tools follow a clear verb_noun pattern (add_resource, create_project, query_records, write_record), but several tools use noun phrases instead (heartbeat, snapshots, project_info, uptime_monitor, custom_route). The split is readable but not a consistent convention.

Tool Count5/5

14 tools is a reasonable, well-scoped size for a combined mock-API platform and monitoring utility. Each tool has a distinct job, and the monitoring tools complement the mock-API lifecycle tools without feeling redundant.

Completeness4/5

The toolset covers project creation/deletion, resource seeding, record CRUD, request inspection, snapshots, and external API monitoring. Minor gaps exist: resources can be added but not individually removed/updated, and custom routes have no delete or update path.

Resources