SwishrJobs

SQL to dataframe migration

submit_sql_to_dataframe_migration

Idempotent

Translate a SQL query into a pandas transform module plus a parity test that runs on your sample. When moving a query out of a warehouse into dataframe code. Do not choose this when the query uses joins, CTEs or window functions. Example requests: Convert this SQL query to pandas. | Migrate this query to a dataframe transform. | Give me the pandas equivalent of this SQL.

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_sql_to_dataframe_migration"
  }
}

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

A3.9/5.0

Behavior3/5

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

Annotations already declare readOnlyHint=false, destructiveHint=false, idempotentHint=true and a closed-world profile, so the safety story is largely covered structurally. The description adds useful behavior detail by explaining that the output includes a parity test exercising the sample rows, but it says nothing about the priced-job/quote authorization flow or turnaround expectations.

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?

It is front-loaded with the deliverable, then the usage trigger, then the exclusion, which is the right order. The trailing 'Example requests' list is largely redundant with the opening sentence and is the one part that does not earn its space.

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-value explanation is not needed, and the description covers the core artifact and its scope limit. The remaining gap is the commercial wrapper (quote issuance and price acceptance) required by two of the four parameters, though the schema documents those fields directly.

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%, so most parameter meaning is already in the structured fields. The description does add one genuinely new link — that 'sample' is the data the parity test runs against — but it is silent on quote_id, accepted_price and authorized_by, which are the parameters most likely to cause a failed call.

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 specific verb and a specific pair of artifacts: a pandas transform module plus a parity test run against the supplied sample. That is far more precise than the sibling 'submit_csv_*' or 'submit_json_*' converters, so an agent can tell what this produces without opening the schema.

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?

It gives a clear trigger ('when moving a query out of a warehouse into dataframe code') and an explicit exclusion ('Do not choose this when the query uses joins, CTEs or window functions'). It stops short of naming a sibling to use instead when the query is join-heavy, so it falls just under the top mark.

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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