Skip to main content
Glama
BlueWaterCorp

RiskModels

by BlueWaterCorp
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

RiskModels Long/Short Portfolio Exposure

riskmodels_portfolio_exposure
Read-only

Calculate portfolio beta-dollars by sector and ETF hedge trades that neutralize short or 100+ name books, with systematic/residual dollar risk, coverage, and warnings.

Instructions

Use when a book has short positions or more than 100 names, or when you want its beta-dollars and the ETF trades that neutralise it. Not for long-only books of up to 100 names given as weights (use riskmodels_analyze_portfolio). Returns, from POST /portfolio/exposure, beta-dollars by sector, the ETF trades that neutralise the book at L1, L2 and L3, systematic and residual dollar risk at hedge_level (default lstar: each name at its own recommended depth), coverage (positions not modelled, with reasons) and warnings. Positions are netted by security and not rescaled. End-of-day data, refreshed after each US market close. Price: $0.25 per call up to 25 modelled names, $1.00 above.

Input Schema

NameRequiredDescriptionDefault
as_ofNoPast model date, 2011-12-07 or later; before 2013-01-01 lstar uses L3 (default latest), format YYYY-MM-DD, e.g. 2024-12-31
positionsYesSigned book of 1-1000 positions, e.g. [{"ticker":"AAPL","value":100000},{"ticker":"TSLA","value":-50000}]
hedge_levelNolstar: each name at its own recommended depth; l1/l2/l3: one depth for every name, e.g. l3 (default lstar)
lookback_daysNoTrading days in the ETF covariance window, integer 60-756, e.g. 126 (default 252)

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed6 schema fields changedv1.0.12
    • changedInput schema / properties / as_of / description
      Previous value: -"Past model date YYYY-MM-DD (2011-12-07 or later; before 2013-01-01 lstar uses L3); omit for the latest"New value: +"Past model date, 2011-12-07 or later; before 2013-01-01 lstar uses L3 (default latest), format YYYY-MM-DD, e.g. 2024-12-31"
    • changedInput schema / properties / hedge_level / description
      Previous value: -"lstar (default): each name at its own L* level. l1/l2/l3: one level for every name"New value: +"lstar: each name at its own recommended depth; l1/l2/l3: one depth for every name, e.g. l3 (default lstar)"
    • changedInput schema / properties / lookback_days / description
      Previous value: -"Trading days in the ETF covariance window (default 252)"New value: +"Trading days in the ETF covariance window, integer 60-756, e.g. 126 (default 252)"
    • changedInput schema / properties / positions / description
      Previous value: -"Signed book: { ticker, value } in dollars, short < 0 (1-1000)"New value: +"Signed book of 1-1000 positions, e.g. [{\"ticker\":\"AAPL\",\"value\":100000},{\"ticker\":\"TSLA\",\"value\":-50000}]"
    • addedInput schema / properties / positions / items / properties / ticker / description
      Added value: +"US stock ticker symbol, e.g. TSLA"
    • changedInput schema / properties / positions / items / properties / value / description
      Previous value: -"Signed dollar market value; short < 0"New value: +"Signed market value in US dollars, short < 0, e.g. -50000"
  2. First observedv1.0.11

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, but the description adds behavior the annotations cannot: positions are netted by security and not rescaled, data is end-of-day refreshed after each US close, the response includes coverage gaps with reasons and warnings, and pricing is given ($0.25 up to 25 names, $1.00 above). This is exactly the beyond-annotation context the dimension rewards.

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?

Usage routing is front-loaded in the first two sentences, followed by returns, semantics, freshness, and price. Each sentence carries distinct information (routing, output shape, netting behavior, data cadence, cost), so nothing is wasted.

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?

There is no output schema, but the description enumerates the return payload (beta-dollars by sector, L1/L2/L3 ETF trades, systematic and residual dollar risk, coverage, warnings) and covers freshness, cost, and netting rules. Complete for an agent to call it correctly.

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?

Schema description coverage is 100%, so all four parameters are documented in the schema itself; the description largely echoes the hedge_level default of lstar without adding new syntax. The only extra semantic, that positions are netted by security, is more behavioral than parameter-level. Baseline 3 is appropriate.

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 and resource ('returns beta-dollars by sector, the ETF trades that neutralise the book at L1, L2 and L3, systematic and residual dollar risk') and explicitly contrasts itself with the sibling riskmodels_analyze_portfolio. An agent can route between the two without opening either schema.

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?

Gives an explicit trigger ('when a book has short positions or more than 100 names, or when you want its beta-dollars') and an explicit exclusion with a named alternative ('Not for long-only books of up to 100 names given as weights (use riskmodels_analyze_portfolio)'). When-to, when-not, and the substitute are all present.

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

Deploy Server

Other Tools