quantifyme

Get Deploy Result

get_deploy_result

Read-onlyIdempotent

Wait for a one_shot deploy to finish and return its final result.

`one_shot` returns a job_token immediately and the LIVE CARD already streams
progress and renders the interactive backtest chart itself. Call this ONCE with
the token to get the final numbers as TEXT so you can summarize them — it does
NOT render another card (no need for get_model_chart). It BLOCKS until the deploy
finishes (or ~2.5 min); on timeout it returns ok:false + pending:true — call it
again with the same token.

IMPORTANT: if `source == "community"`, the deploy used a PRE-EXISTING strategy by
`@author` — tell the user that, share the `live_url` as the Live dashboard link,
and ask whether they'd like to GENERATE A CUSTOM strategy instead. Use the
`note` field as your guide.

Args:
  job_token: the token returned by `one_shot`.

Returns:
  dict with: ok, stem, model, live_url, symbol, timeframe, channels (list),
  stats:{ret, wr, pf, n, mdd} (out-of-sample test-split metrics — SHOW THESE),
  source ("community" | "generated"), author (community username if any),
  author_url + strategy_url (render @author and "pre-existing strategy" as those
  Markdown links), community_id, suggest_custom (bool), and note (a ready
  instruction — follow it). On failure: {ok:false, error} (or {pending:true}).

Input Schema

TableJSON Schema

Name	Required	Description	Default
`job_token`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A4.6/5.0

Behavior5/5

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

Annotations indicate readOnlyHint, idempotentHint, and openWorldHint. The description adds crucial behaviors: it BLOCKS until finish (~2.5 min), returns pending:true on timeout, and does not render a card. No contradiction with annotations.

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?

The description is well-structured with paragraphs and bullet points, front-loading the main purpose. While fairly long, every sentence provides necessary information. Could be slightly more concise but overall effective.

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?

Given the tool's complexity (blocking, async, multiple result cases), the description covers the return dict with all fields, error/timeout handling, and community-source behavior. The output schema exists, and the description adequately supplements it.

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?

The sole parameter `job_token` has 0% schema description coverage. The description adds meaning by stating it is 'the token returned by one_shot', linking to the sibling tool. This is helpful but minimal, thus a score of 3.

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 clearly states the tool's purpose: 'Wait for a `one_shot` deploy to finish and return its final result.' It specifies the action (wait and return result), the resource (deploy result), and distinguishes from sibling `get_model_chart` by noting it does not render another card.

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?

The description provides explicit guidance: call ONCE after `one_shot`, do not use `get_model_chart`, handle timeout by calling again, and specific instructions for community-source deploys. It clearly states when to use and what to avoid.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All quantifyme tools Inspect

Try in Browser

RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.9/5.0

Disambiguation4/5

Most tools have clearly distinct purposes, though browse_community, find_strategy, and one_shot (community_id) are related and could cause confusion if descriptions are not read carefully. Overall well-differentiated.

Naming Consistency4/5

Overwhelmingly follows a verb_noun pattern (browse_community, find_strategy, generate_strategy, get_deploy_result, etc.). A few exceptions like 'one_shot' and 'top_up' break the pattern but are still clear and memorable.

Tool Count4/5

13 tools cover the major functions of a trading strategy platform (discover, generate, deploy, monitor, account management). The count feels appropriate, though 'stream_test' is diagnotic and may not be needed in all contexts.

Completeness3/5

Covers core workflows well—browse, find, generate, deploy, and get results. However, lacks tools to update or stop a deployed model, and there's no way to edit an existing strategy, leaving some lifecycle gaps.

Resources

Need Help?