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sablier-ai

Sablier MCP Server

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by sablier-ai

validate_rules

Read-only

Preflight check trading rules for schema errors, missing portfolio assets, and feature coverage before backtests, returning a detailed error list to enable quick fixes.

Instructions

Preflight validation of stored rules: schema-checks each rule's trigger and action grammar, verifies referenced assets exist in the portfolio, and (if flow_job_id given) confirms every feature the rules reference is covered by the FLOW model's feature set. Run this before backtest_rules / forward_test_rules to surface bad rules cheaply (~200ms, free) instead of letting them silently fail mid-backtest. Returns ok=true with empty error lists if all clean; otherwise lists missing_portfolio_assets, missing_rule_features, and per-rule grammar errors so the agent can patch and retry.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rule_idsNoSpecific rules to check (default: all rules in the portfolio)
flow_job_idNoFLOW generation job to feature-coverage-check against (optional)
portfolio_idYesPortfolio UUID

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior5/5

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

Beyond the readOnlyHint annotation, the description adds performance characteristics (~200ms, free), return behavior (ok=true with error lists), and specifics about what gets checked (missing_portfolio_assets, missing_rule_features, per-rule grammar errors). This is rich behavioral context with no contradiction.

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?

Two sentences, front-loaded with the primary purpose, followed by usage guidance and return details. Every clause earns its place with zero fluff.

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 output schema exists and annotations cover read-only safety, the description fully covers what the tool does, when to use it, performance expectations, and return format. No significant gaps.

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

Parameters4/5

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

Schema coverage is 100%, so baseline is 3. The description adds semantic context for flow_job_id (feature coverage check) and rule_ids (scope selection), reinforcing schema descriptions. It adds some value but does not deeply expand beyond what the schema already conveys.

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 validates stored rules via schema checks, asset existence, and optional feature coverage. It distinguishes itself from siblings like backtest_rules and forward_test_rules by explicitly mentioning this is a preflight validation step.

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 says 'Run this before backtest_rules / forward_test_rules' and explains the benefit (~200ms, free, avoids silent mid-backtest failures). This gives clear when-to-use and names the alternatives.

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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