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create_project

Create a new mock REST API project. Returns {id, adminKey, baseUrl, resources[]}. SAVE the adminKey — it is required for admin operations (add_resource, custom_route, snapshots) and is shown only once. Presets seed a full backend: blog (posts/comments/authors), ecommerce (products/orders/customers/reviews), saas (users/teams/events), openai (ready OpenAI-compatible mock — chat completions incl. streaming SSE, embeddings with a real 1536-dim vector, models; point OPENAI_BASE_URL at {baseUrl}/v1). Omit preset for a starter project (one seeded "items" resource — live data immediately, reshape or delete it); use "blank" for a truly empty project you fill via add_resource or import_data. The mock API is then live at baseUrl: standard REST CRUD (GET/POST/PUT/PATCH/DELETE), CORS enabled, no auth needed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoProject name (max 60 chars). Optional.
presetNoSeeded preset; 'blank' = truly empty. Omit for a starter project. Optional.

Schema Changelog

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

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

With no annotations provided, the description carries the full behavioral disclosure burden. It is transparent about the one-time display of adminKey, the live baseUrl behavior, standard REST CRUD capabilities, CORS enabled, no auth needed, and the streaming/embedding behavior of the openai preset.

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 every clause earns its place: return fields, adminKey warning, preset definitions, starter vs blank behavior, and live endpoint characteristics. It front-loads the most critical information about the returned values and the one-time credential.

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?

For a tool with no output schema and no annotations, this description is operationally complete. It specifies what is created, what is returned, how to choose a preset, what the resulting API supports, and what must be saved for later admin operations. There is no critical gap for an agent deciding whether and how to call it.

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

Parameters5/5

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

While the schema already covers both parameters at 100%, the description adds substantial meaning: it explains exactly what each preset seeds, contrasts omitting the preset with using 'blank', and clarifies that name is optional. This goes well beyond the enum labels and schema descriptions.

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 opens with a specific action and resource: 'Create a new mock REST API project.' It also names the return contract ({id, adminKey, baseUrl, resources[]}) and explains the main variants (preset, starter, blank), making it easy to distinguish from sibling operations like add_resource or project_info.

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

Usage Guidelines5/5

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

It gives explicit guidance on when to omit the preset versus use 'blank', and names add_resource and import_data as the follow-up tools for building out a project. It also flags that the adminKey is required for admin operations such as add_resource, custom_route, and snapshots, helping the agent plan subsequent calls.

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