Skip to main content
Glama

Mockbird

by dev.workers.mockbird.mockbird

add_resource

Add a resource (collection) to a project and seed it with realistic fake data. Either pass template (one of the built-ins, e.g. users, products, posts, comments, orders, todos, reviews, customers, events) or fields: an array of {name, type} where type ∈ uuid|firstName|lastName|fullName|username|email|avatar|image|word|words|title|sentence|paragraph|number|price|percent|boolean|date|pastDate|futureDate|url|domain|ip|phone|city|country|address|zipCode|company|jobTitle|color|latitude|longitude|rating|age|slug|status|category|refId, plus {name, type:"oneOf", values:[...]} for enums. seed = number of records to generate (default 20, max 100, 0 = empty).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesResource name, plural (e.g. products).
seedNoRecords to seed (default 20, max 100).
fieldsNoArray of {name, type} (or {name, type:'oneOf', values:[…]}). Optional.
projectYesProject id.
adminKeyYesThe project's adminKey.
templateNoBuilt-in template name. Optional (use this OR fields).

Schema Changelog

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

  1. First observed

TDQS

A3.9/5.0
Behavior3/5

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

With no annotations, the description must carry the full burden of behavioral disclosure. It does state that the tool creates a resource and seeds it with fake data, and it clarifies seed count semantics (default, max, empty). However, it does not address side effects like overwriting behavior, idempotency, required permissions beyond adminKey, or failure modes, leaving some behavioral aspects opaque.

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 front-loaded with the main purpose, then logically breaks down the two modes (template vs fields) and seed behavior. It is dense and lengthy, especially the field-type enumeration, but that list is directly actionable for an agent deciding whether to use custom fields, so the detail 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?

For a tool with 6 parameters, no annotations, and no output schema, the description covers the key aspects: what the tool does, the two input modes, supported data types, and seed limits. It could be more complete by explaining return values or duplicate-resource behavior, but it is largely sufficient for an agent to invoke it correctly.

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 substantial value beyond the schema by enumerating valid template examples, listing the full set of accepted field types, explaining the oneOf enum construct, and detailing seed constraints. This goes well beyond the schema's brief 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 verb ('Add') and resource ('resource/collection'), clearly distinguishing this from sibling tools like import_data or write_record. It further clarifies that the tool seeds the resource with fake data, making its scope unambiguous.

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 gives clear within-tool guidance on choosing between template and fields, and specifies seed behavior. However, it does not explicitly contrast this tool with siblings such as import_data or write_record, nor does it state when not to use it. Usage is implied by the name and primary action rather than stated as explicit selection criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Mockbird toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

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