SwishrJobs

Markdown table renderer with pytest tests

submit_markdown_table_rendering

Idempotent

Produce a standard-library Python module that renders JSON records as a Markdown table, plus pytest tests. Choose this when JSON records must become a GitHub-flavoured Markdown table with pipes escaped. Do not choose this for HTML tables, spreadsheet formats, or column alignment rules beyond the template. Example requests: Write a Python module that renders JSON records as a GitHub Markdown table, with tests. | I need a Markdown table renderer that escapes pipes so data cannot break the table. | Generate a Markdown-table module and test it.

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_markdown_table_rendering"
  }
}

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?

Annotations already declare readOnlyHint=false, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered and the description does not contradict it. However, the description omits the material behavior of this tool: that it submits a job, that the work is priced, and that a quote must be authorized before the job is accepted (left entirely to the schema's quote_id/accepted_price docs). With annotations partially covering the profile, this is adequate but short of rich context.

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?

Front-loaded with the deliverable, followed by inclusion and exclusion criteria, with no filler. The three example requests restate essentially the same intent and add little beyond the prose, which keeps it from a 5.

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 explained, and the nested inputs object is documented in-schema. The description is complete on what the generated artifact must do; it is only thin on the job/pricing/authorization flow that surrounds invocation, which the schema partly covers.

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%, so module_name, function_name, template, quote_id and accepted_price are largely documented in-schema. The description adds nothing parameter-specific — no mention of module_name/function_name constraints or of the template constraint 'markdown-table' — so the baseline 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?

States a concrete verb and deliverable: 'Produce a standard-library Python module that renders JSON records as a Markdown table, plus pytest tests.' The GFM/pipe-escaping scope is explicit, and the sibling space is narrowed by naming what it is not (HTML tables, spreadsheets, alignment rules).

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?

'Choose this when JSON records must become a GitHub-flavoured Markdown table with pipes escaped' plus an explicit 'Do not choose this for HTML tables, spreadsheet formats, or column alignment rules beyond the template' gives both inclusion and exclusion criteria. Alternatives are not named as sibling tool names, but the negative conditions are precise enough to route correctly.

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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