SwishrJobs

Object key normalisation module with pytest tests

submit_key_normalisation

Idempotent

Produce a standard-library Python module that rewrites JSON object keys to lower snake_case, plus pytest tests. Choose this when object keys must become lower snake_case, with values left untouched. Do not choose this expecting acronym splitting: a capital run like HTTPServer becomes httpserver, not http_server. Example requests: Write a Python module that rewrites JSON object keys to lower snake_case, with tests. | I need camelCase keys converted to snake_case without changing the values. | Generate a key-normalisation module and a pytest suite.

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_key_normalisation"
  }
}

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

A3.9/5.0

Behavior4/5

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

Adds real behavior beyond the annotations: standard-library only, values untouched, deterministic acronym collapsing (HTTPServer -> httpserver), and a required pytest suite. Annotations already cover readOnly/idempotent/destructive, so this extra semantic detail is a genuine contribution.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Purpose and selection guidance are front-loaded, which is good. However, three 'Example requests' restate the opening sentence almost verbatim, adding bulk rather than new information.

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

Completeness3/5

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

For a job-submission tool that creates work and consumes a priced quote, the description says nothing about the quote/authorization flow (quote_id, accepted_price) or that this submits rather than returns the module directly. The output schema covers return values, so that omission is fine.

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 coverage is 75% and the nested inputs object documents module_name, function_name and template with patterns, so the schema carries most of the load. The description adds nothing about any parameter, notably never explaining the template/quote_id/authorized_by flow.

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?

States a specific verb and artifact: 'Produce a standard-library Python module that rewrites JSON object keys to lower snake_case, plus pytest tests.' That is precise enough to distinguish it from sibling submission tools such as submit_field_mapping or submit_nested_flattening, whose transformations differ.

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?

Explicit selection guidance: 'Choose this when object keys must become lower snake_case, with values left untouched,' plus a concrete negative case ('Do not choose this expecting acronym splitting'). It does not name an alternative sibling to use instead, which keeps it from a 5.

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

All SwishrJobs tools Inspect

Try in Browser

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?