QuanterLab

start_primitives_run

Run one of your saved lab projects (a circuit on the canvas, its latest version) and return a task id to poll. The project runs as saved, at its point-in-time anchor, and takes no settings but index. Its card draws the project's own circuit as the lab draws it, lights each card as it runs and shows the result under it. Get a project id from list_my_primitives_projects, then poll get_primitives_run_progress: when the run has finished its answer carries result, each branch of the project under the card name the project gives it, whether it beat simply holding what it trades over the same dates, and, for a branch tested over a year or longer, a few rounded figures.

    Every account runs it within a daily allowance; a refused call does not count, and get_started and
    every run's answer give today's count.
    A paper-trade card runs as a marker in the test; start_paper_trade with the run's task_id tracks the
    project live, on paper, with a note on it every morning.

    The circuit and its settings stay on QuanterLab. This run's curves are in its report
    (`report_url`, below); `result.lab_url` opens the project on the canvas, where the person
    changes it and saves it.

    When the run has finished, its progress answer also carries `report_url`: the run written up
    as a QuanterLab report (the circuit drawn, each branch that trades against simply holding and
    SPY, and what followed each event of an event-study card). The circuit card shows the result
    under the circuit, with the next steps as choices and the report link, so a run started here
    needs no `show_report_card`.

    Args:
        project_id: The `id` of one of your saved lab projects.
        question: Optional. One sentence restating what the run should answer, not the person's message:
            the title of the report, and kept only as that.
        index: Optional. sp500, nasdaq100 or dow30: the project's cards that pick their stocks from an
            index QuanterLab has today's members of only (the DAX, the CAC 40, the FTSE 100, the EURO
            STOXX 50, the Hang Seng, the Nikkei 225) pick from this US index instead, its members as they
            were on each date, for this run only; the saved project is left as it is. QuanterLab's note
            offers it after such a run.

Input Schema

Name	Required	Description	Default
`index`	No
`question`	No
`project_id`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.5/5.0

Behavior5/5

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

Annotations only declare non-readonly, non-destructive, non-idempotent; the description adds real behavior: the project runs exactly as saved at a point-in-time anchor, the `index` override applies for this run only and leaves the saved project untouched, and there is a daily allowance where refused calls don't count. That rate-limit and no-side-effect-on-project detail is exactly what annotations cannot convey.

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?

The core call-routing information is front-loaded, but the middle is bloated with UI narration ('lights each card as it runs', 'next steps as choices') and `report_url` is introduced twice, first as '(report_url, below)' and again in its own paragraph. Several sentences could be cut without losing agent-relevant meaning.

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

Completeness5/5

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

With no output schema and no schema descriptions, the description nonetheless covers the full lifecycle: how to obtain the id, what the task id is for, what the progress answer will contain (result, per-branch comparison to holding, report_url, lab_url), and the quota constraint. Nothing needed to invoke or follow up is missing.

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 0%, so the description has to carry all three parameters, and it does: project_id's source, question's purpose (one-sentence restatement, used only as the report title), and index's three allowed values plus the DAX/CAC/FTSE substitution behavior. That is genuinely useful content, but the `index` guidance is buried in a dense run-on sentence rather than crisply enumerating the enum.

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 resource ('Run one of your saved lab projects... return a task id to poll') and pins down the scope: the circuit at its point-in-time anchor, no settings but `index`. It is immediately distinguishable from sibling starters like start_paper_trade and start_backtest.

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 routes the agent: get `id` from `list_my_primitives_projects`, poll `get_primitives_run_progress`, and notes that a run started here needs no `show_report_card`. It also names the alternative path (start_paper_trade with the run's task_id for live paper tracking).

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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