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get_preset

Retrieve a named interest-rate curve preset, such as USD_SOFR_OIS or EUR_ESTR_OIS, to get its index definition, curve settings, helper conventions, and field provenance for QuantLib pricing.

Instructions

One preset as data: index definition, curve settings, every helper convention block and the provenance of each field.

Args: id: e.g. USD_SOFR_OIS, EUR_ESTR_OIS, GBP_SONIA_OIS, GBP_SONIA_SWAP, EUR_EURIBOR_6M, EUR_EURIBOR_3M.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are provided, so the description carries the behavioral burden. It conveys that this is a read returning structured preset data, but does not state what happens for an unknown id, whether presets are immutable or session-scoped, or any auth/rate considerations. Adequate but incomplete for a zero-annotation tool.

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 summary sentence is front-loaded and dense with useful detail, and the Args block is short. The example list is a touch long but each entry earns its place by demonstrating the id pattern.

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

Completeness4/5

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

With an output schema present, the description needn't explain return values, yet it does sketch them, and the id examples cover the only parameter. For a one-parameter lookup this is nearly sufficient; the missing usage routing is the main gap.

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 description coverage is 0%, so the description must compensate, and it does by giving six concrete id examples (USD_SOFR_OIS, EUR_ESTR_OIS, GBP_SONIA_OIS, EUR_EURIBOR_6M, etc.) that reveal the naming convention. It does not say whether ids are case-sensitive or where the canonical list comes from, but the examples add real value.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific resource (one preset) and enumerates its contents (index definition, curve settings, helper convention blocks, field provenance), which is far more informative than the bare name. It does not explicitly name list_presets as the plural sibling it differs from, so it falls short of the 5 bar.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no statement of when to use this versus list_presets or the various build/bootstrap/calibrate tools. The Args block documents the id, not the usage context, so an agent must infer the retrieval scenario entirely.

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