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ms-methos

jsonfabrica-mcp-server

Generate a document from a raw template body (no persistence)

jsonfabrica_generate_adhoc

Generate a JSON document on the fly from a raw body string to test template syntax without saving a reusable template. Set debug namespaces to isolate sequence side effects.

Instructions

Calls POST /v1/templates/generate. Generates a document directly from a raw body string without creating a template record — useful for quickly iterating on template syntax. Goes through the same billing/usage metering as persisted-template generation (not a free bypass). createSeq()/durable sequence side effects still apply; set sequenceNamespace/ variableNamespace to e.g. "debug" to avoid colliding with real tenant sequences.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
bodyYes
seedNo
paramsNo
contextNo
sequenceNamespaceNo
variableNamespaceNo

Schema Changelog

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

  1. First observedv0.1.4

TDQS

A4.4/5.0
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 an excellent job: it discloses that no template record is created, that billing/usage metering still applies, and that createSeq()/durable sequence side effects still occur with advice on avoiding namespace collisions. This goes well beyond the obvious and is genuinely useful for safe invocation.

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?

Three sentences with no filler: endpoint, core behavior and use case, then the two most important caveats. The information is front-loaded and 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?

The description is strong for a complex tool with no annotations and no output schema: it covers side effects, billing, and namespace safety. The main gaps are the unaddressed seed/params/context parameters and the lack of any detail about the response shape, but the core invocation guidance is solid.

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?

The input schema has 0% description coverage, but the description adds meaning for body (raw template string) and for sequenceNamespace/variableNamespace (isolation from real tenant sequences). However, seed, params, and context remain unexplained, so the description only partially compensates for the lack of schema documentation.

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 action: it calls POST /v1/templates/generate and produces a document from a raw body string without persisting a template. This directly distinguishes it from siblings like jsonfabrica_create_template and jsonfabrica_generate_from_template, so an agent can tell them apart.

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?

It explicitly frames the tool as useful for quickly iterating on template syntax and warns that it is not a billing bypass compared to persisted-template generation. It does not explicitly name alternative tools or say 'use X instead', but the intended context is clear.

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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