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sablier-ai

Sablier MCP Server

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by sablier-ai

create_portfolio

Destructive

Create a portfolio from tickers using equal or custom weights, with auto-ingestion for unknown symbols and support for portfolios up to 1,000 assets.

Instructions

Create a new portfolio from tickers. Two payload shapes: • Explicit weights: pass tickers AND weights (parallel arrays, weights must sum to 1.0). • Equal weight: pass tickers only with equal_weight=True — server applies 1/N each.

Use equal_weight=True for ANY portfolio over ~50 assets. The parallel-array shape blows past LLM tool-call output budgets around 60-100 entries: one array gets truncated mid-generation and you'll see 'tickers and weights must have the same length' even though you generated them at the same size. A 500-ticker single-list call is ~10× smaller and reliable.

For CSV-paste / large-portfolio flows: combine equal_weight=True with auto_add=True to have the server auto-ingest unknown tickers via Yahoo Finance, and skip_missing=True to drop the ones yfinance rejects. The result includes import_summary with added_from_catalog / newly_ingested / dropped (with reasons) — report all three counts back to the user.

Size: up to ~1000 assets per portfolio (tier limit is 999,999 — effectively unbounded). If a user asks for a 500-asset or 1000-asset portfolio, build it. Do not refuse, do not lecture about 'focused portfolios', do not suggest ETF buckets unless the user explicitly asks for construction advice. Large portfolios are a supported, intentional product surface (F=1000 FLOW models train in minutes on GPU; analytics stay fast via precomputed bands + virtualized UIs).

Non-USD tickers are accepted — their prices are auto-translated to USD and the FX pair is fetched on-demand by add_feature. Resulting returns are USD-denominated and reflect the same FX exposure the underlying stock carries; do NOT warn the user about a 'currency mismatch' against a USD-quoted equivalent (DR/ADR/ETF/fund) — the exposure is economically the same.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesPortfolio name (e.g. 'Tech Portfolio')
capitalNoTotal capital allocation in USD (default $100,000)
tickersYesTicker symbols (e.g. ['AAPL', 'MSFT', 'NVDA'])
weightsNoCorresponding weights summing to 1.0 (e.g. [0.4, 0.3, 0.3]). Omit (or pass null) together with equal_weight=True to apply 1/N each — required for portfolios over ~50 assets to avoid LLM tool-call output truncation.
auto_addNoAuto-ingest unknown tickers via Yahoo Finance before classifying as missing. Use with CSV-paste flows.
descriptionNoOptional description
equal_weightNoIf true, server applies 1/N weight to each ticker (mutually exclusive with `weights`).
skip_missingNoDrop tickers that aren't in the catalog (and aren't ingested by auto_add) instead of failing the call.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the destructiveHint annotation, the description discloses server-side behavior: Yahoo Finance auto-ingestion, dropping missing tickers, import_summary counts, USD FX translation, and tier limits. It also explains operational consequences like truncation risk, giving the agent context that annotations alone cannot provide. 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.

Conciseness5/5

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

The description is long but dense with purposeful information. It is front-loaded with the core purpose, then structured into clear sections: payload shapes, large-portfolio guidance, CSV-paste flows, size limits, and currency behavior. Every sentence earns its place, and bolding/scannable formatting improves readability.

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 complex tool with 8 parameters and a meaningful output schema, the description covers all essential operational context: payload shape selection, scale guidance, unknown-ticker handling, import summary reporting, size limits, and currency semantics. This is the full picture an agent needs to invoke the tool correctly.

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?

Although the schema has 100% coverage, the description adds critical semantics: weights must sum to 1.0, equal_weight applies 1/N, the two payload shapes are mutually exclusive, and parallel arrays risk truncation for large portfolios. It also explains how auto_add and skip_missing interact, far exceeding the schema's basic field descriptions.

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, 'Create a new portfolio from tickers', and immediately differentiates itself by explaining two payload shapes. This clearly distinguishes it from sibling tools like update_portfolio and delete_portfolio.

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 on when to use equal_weight=True for portfolios over ~50 assets, when to combine auto_add and skip_missing for CSV-paste flows, and explicitly tells the agent not to refuse large portfolios. It also names the alternative pattern to avoid and what not to warn users about, going well beyond basic usage.

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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