SwishrJobs

Log format parser generator

submit_log_format_parser

Idempotent

Infer a log format from sample lines and deliver a Python parser module that extracts timestamps, addresses and bracketed fields, plus the parsed samples as JSON. When you have sample logs and want a working parser rather than a regex to write by hand. Do not choose this when the log format is already known and a library parser exists. Example requests: Write me a parser for this log format. | Generate a Python module that parses these log lines. | What fields can be extracted from these logs?

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_log_format_parser"
  }
}

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.

AddedNov 3, 2026

TDQS

A4/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=false, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds that the result is a generated module plus JSON samples, which is useful, but it says nothing about the priced/authorized job workflow that the schema implies (quote_id, accepted_price, authorized_by) or about limits such as the 1 MiB sample cap. Adequate, not rich.

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?

Purpose and output come first, then the when/when-not guidance, then examples, which is a sensible front-loaded order. The trailing pipe-separated example requests are somewhat padding-like and could be trimmed, but nothing is seriously wasteful.

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?

An output schema exists so return values need no explanation, and most parameters are documented in the schema. However, this is a priced, authorized mutation-style submission that sits alongside preflight_job and submit_job, and the description never mentions that a quote must be obtained or how authorization is supplied. For a job-submission tool that is a meaningful context gap.

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 the schema largely documents samples, quote_id, accepted_price and authorized_by itself. The description adds no meaning about sample format expectations, quoting behavior or authorization, so it sits at the baseline of 3 for high-coverage schemas.

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?

"Infer a log format from sample lines and deliver a Python parser module" gives a specific verb (infer/generate) and a concrete resource (Python parser module), then names the extracted artifacts (timestamps, addresses, bracketed fields) and the secondary output (parsed samples as JSON). An agent can distinguish it from submit_text_parser_module or submit_regex_from_examples purely on stated inputs and outputs.

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 states the positive condition ("When you have sample logs and want a working parser rather than a regex to write by hand") and an explicit exclusion ("Do not choose this when the log format is already known and a library parser exists"), plus three example requests that anchor the intent. This is close to the maximum guidance an agent needs.

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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