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JSON Schema inference module with pytest tests

submit_json_schema_inference

Idempotent

Produce a standard-library Python module that infers a JSON Schema from sample records, plus pytest tests. Choose this when you need a draft 2020-12 JSON Schema inferred from sample objects, with keys present in every record marked required. Do not choose this for a hand-tuned schema or for constraints the samples do not express (formats, ranges, enums). Example requests: Write a Python module that infers a JSON Schema from sample records, with tests. | I need a JSON Schema generated from example objects, marking common keys as required. | Generate a schema-inference module and prove it with pytest.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "inputs": "<inputs>",
      "authorized_by": "<authorized_by>"
    },
    "name": "submit_json_schema_inference"
  }
}

Input Schema

Name	Required	Description
`inputs`	Yes
`quote_id`	No	The quote_id returned by POST /v1/jobs. Required to authorize a priced capability; the service rejects a priced job that does not name the quote it issued.
`authorized_by`	Yes	Identifier of the human or agent authorizing this job.
`accepted_price`	No	The amount accepted, exactly as quoted (for example "5.00" or 5). Checked against the stored quote, so a price that moved between quote and authorize is refused rather than silently absorbed. Optional, but sent to make the acceptance explicit.

Output Schema

Name	Required	Description
`quote`	No
`state`	Yes
`job_id`	Yes
`artifacts`	No
`access_token`	No	Per-job credential. Present it as the X-Job-Token header to read the job or list it. Keep it; it is the only credential for this job.
`verification`	No	Independent verification. verification.ok = false means the job failed, not a partial success.
`capability_id`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 31, 2026

TDQS

A4.1/5.0

Behavior3/5

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

The annotations already declare readOnlyHint=false, idempotentHint=true and destructiveHint=false, and the description usefully explains that behaviour is fixed by the template and verified by its own tests. However, it says nothing about the priced-capability authorization flow implied by the required quote_id and accepted_price fields, which is the one behaviour an agent could get wrong here.

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 core scope, the positive selection rule and the negative rule are front-loaded in two tight sentences. The three example requests are helpful for matching user phrasing but are largely restatements of the preceding guidance, so the section is slightly padded.

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?

An output schema exists, so return values need not be described, and the deliverable and constraints are clear. The main omission is the quote-and-authorize workflow that the required quote_id and accepted_price imply, which an agent would need to know before calling.

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 75% and every input property (template, module_name, function_name, quote_id, accepted_price, authorized_by) already carries its own description. The description adds no argument-level detail beyond noting that the template fixes the behaviour, so the baseline of 3 applies.

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 names a concrete verb and artifact ('produce a standard-library Python module that infers a JSON Schema from sample records, plus pytest tests') and pins the target dialect (draft 2020-12) with the required-key rule. That is enough to separate it from the sibling validation/quality tools without opening any schema.

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 an explicit selection rule ('Choose this when you need a draft 2020-12 JSON Schema inferred from sample objects'), an explicit exclusion ('Do not choose this for a hand-tuned schema or for constraints the samples do not express (formats, ranges, enums)'), and three sample user phrasings for routing.

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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