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Disco

by com.leap-labs

discovery_get_results

Read-only

Fetch the full results of a completed Disco run.

Returns discovered patterns (with conditions, p-values, novelty scores,
citations), feature importance scores, a summary with key insights, column
statistics, and suggestions for what to explore next.

The response includes a `dashboard_urls` object with direct links to each
page of the interactive report — use these to direct the user to the most
relevant view:
- **summary**: AI-generated overview with key insights, novel findings, and plain-language explanation of the most important findings
- **patterns**: Full list of discovered patterns with conditions, effect sizes, p-values, novelty scores, citations, and interactive visualizations
- **features**: Feature importances, feature statistics and distribution plots, and correlation matrix
- **territory**: Interactive 3D map showing how patterns select different regions of the data

Only call this after discovery_status returns "completed".

Args:
    run_id: The run ID returned by discovery_analyze.
    api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
run_idYes
api_keyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.8/5.0
Behavior4/5

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

Annotations provide readOnlyHint: true, and the description adds the critical behavioral constraint of only being callable after a run completes. It does not contradict annotations. It also describes the structure of the response, including the dashboard_urls object and what each page contains. While auth via api_key is mentioned in params, the description doesn't detail rate limits or error behavior, so a small gap remains.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured: a one-line purpose, a concise list of returned content, a detailed but organized bullet list for dashboard URLs, a clear prerequisite, and a straightforward Args section. Every sentence earns its place and the most important information (purpose and precondition) is front-loaded.

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?

For a tool that returns a complex results object, the description adequately covers response contents, dashboard navigation, the dependency on a completed run, and parameter semantics. The presence of an output schema means it needn't enumerate every field, but it provides enough context for an agent to select and invoke the tool correctly. The only minor gap is lack of error/edge-case handling, but that's not essential.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% (no descriptions in schema for the two parameters), but the description fully documents both: run_id is defined as 'The run ID returned by discovery_analyze,' and api_key is described with format 'disco_...' and the fallback to DISCOVERY_API_KEY env var. This adds essential meaning beyond the bare schema.

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 opens with a specific verb and resource: 'Fetch the full results of a completed Disco run.' It enumerates the returned components (patterns, feature importance, summary, column statistics, suggestions), which distinguishes it from siblings like discovery_status (status checking) and discovery_analyze (starting analysis). The dashboard_urls breakdown further clarifies the tool's scope.

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 states 'Only call this after discovery_status returns "completed".' This gives a clear precondition and directs the user to the alternative status-checking tool. The dashboard_urls section also provides guidance on when to use which result view.

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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TDQS

A4.4/5.0
Disambiguation5/5

Each tool targets a distinct operation (account, payment, analysis, plans, login flows, etc.) with no overlap. Even paired tools like signup/login and their verify counterparts are clearly differentiated.

Naming Consistency5/5

All tools follow a consistent 'discovery_' prefix + verb_noun pattern (e.g., discovery_add_payment_method, discovery_get_results). No mixed conventions or vague names.

Tool Count5/5

14 tools cover account management, analysis pipeline, and billing without unnecessary redundancy. The scope is well-matched to the server's purpose.

Completeness4/5

The tool set covers the full user journey from signup and payment to running analyses and retrieving results. Minor gaps exist (e.g., no explicit cancellation of subscriptions), but the core workflow is fully supported.

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