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Regex builder module with pytest tests

submit_regex_from_examples

Idempotent

Produce a standard-library Python module that builds a regular expression from positive and negative examples, plus pytest tests. Choose this when a regular expression must be derived from examples using the shared literal prefix, correctly escaped. Do not choose this expecting a general regex synthesiser: it uses the longest literal prefix shared by the positive examples, and negative examples are used for checking, not for general inference. Example requests: Write a Python module that builds a regex from positive and negative examples, with tests. | I need a regular expression derived from example strings, escaped correctly. | Generate a regex-builder 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_regex_from_examples"
  }
}

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

A4.3/5.0

Behavior4/5

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

Annotations already disclose the safety profile (non-read-only, idempotent, non-destructive, closed-world), so the description properly focuses on behavior that annotations cannot express: the algorithm uses the longest shared literal prefix and negative examples only validate, not infer. It does not explain the priced-job/quote authorization flow, but the schema covers that parameter directly.

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-loads what is produced, then the selection rule, then the algorithm limitation. The three example requests are useful but slightly redundant with the earlier sentences, keeping it short of maximum conciseness.

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?

Given an output schema, annotations, a nested input schema and a complex code-generation task, the description supplies the crucial algorithmic scope and exclusion. Pricing/authorization context is left to the schema, which is acceptable because it is explicitly documented there.

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 already explains template, module_name, function_name, quote_id, authorized_by and accepted_price. The description adds no parameter-level detail beyond example requests implying the module/test shape, so it neither compensates for nor deepens the remaining gap.

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 concrete output: a standard-library Python module that builds a regex from positive and negative examples, plus pytest tests. This is easily distinguished from generic siblings like submit_python_utility_module because the resource and algorithm are named precisely.

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?

Explicitly says when to choose it ('when a regular expression must be derived from examples using the shared literal prefix') and when not to ('Do not choose this expecting a general regex synthesiser'), including exactly why the limitation exists. That is the when/when-not/alternative pattern at full strength.

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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