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Glama

price_callable_fixed_rate_bond

Price callable or puttable fixed-rate bonds on a Hull-White lattice using supplied market data and call/put schedules.

Instructions

Price a callable / puttable fixed-rate bond on a Hull-White lattice (POST /price-callable-fixed-rate-bond).

Args: market_data_source: where the market numbers in this call come from. user_pasted (the user pasted or typed the numbers in this conversation), user_file (the user attached a file/screenshot the numbers were read from), engine_example (an engine example's pricing block, only when the user explicitly asked to run an example), session (a market previously stored in this session, which itself came from one of the above). There is no value for estimated, recalled or placeholder data. If you would have to invent numbers, do not call this tool: ask the user for the data. preset: a preset with a callable_fixed_rate_bond block (EUR_FIXED_BOND). call_schedule: [{date, price, type: Call|Put}] with increasing dates (clean price per 100 of face). model: {a, sigma, lattice_steps?, id?} (explicit Hull-White, lattice_steps default from the preset) or the id of a SwaptionModelSpec in the market. tree_steps: engine lattice steps for the bond (default from the preset). Other arguments: as in price_fixed_rate_bond.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
as_ofNo
modelYes
tenorNo
marketYes
presetYes
overridesNo
issue_dateYes
request_idNo
tree_stepsNo
coupon_rateYes
face_amountYes
call_scheduleYes
maturity_dateNo
effective_dateNo
additional_tradesNo
discounting_curveYes
calendar_overridesNo
market_data_sourceYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.4

TDQS

A3.8/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. It does valuable work disclosing that estimated/recalled/placeholder market data is forbidden and that session-sourced data must trace to a real origin, but says nothing about permissions, idempotency, or pricing side effects, and does not describe the response.

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?

Front-loaded with purpose and endpoint, then an Args block that is organized and scannable. It is fairly long, and the delegation sentence is terse, but no sentence is pure filler.

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

Completeness3/5

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

For an 18-parameter, 9-required, nested-object tool with no annotations, the description is only partially complete. An output schema exists so return values need no explanation, but the many undocumented parameters and absent behavioral notes leave gaps.

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?

Top-level schema description coverage is 0%, so the description must compensate. It well documents market_data_source, and covers preset, call_schedule shape, model and tree_steps, but leaves the majority of the 18 parameters (face_amount, coupon_rate, discounting_curve, market, overrides, etc.) to an external reference ('as in price_fixed_rate_bond').

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?

States a specific verb (price) and resource (callable / puttable fixed-rate bond) plus the method used (Hull-White lattice) and the underlying endpoint. An agent can distinguish it from price_fixed_rate_bond by the callability dimension.

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

Usage Guidelines4/5

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

The market_data_source paragraph gives explicit when-not-to-use guidance ('If you would have to invent numbers, do not call this tool: ask the user for the data') and constrains the engine_example path. It delegates remaining argument semantics to price_fixed_rate_bond, but does not state callable-vs-plain alternative routing explicitly.

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