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Glama
LeoLourson

MacroStress

by LeoLourson
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
FRED_API_KEYNoAPI key for FRED, needed to download FRED macro series.

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tasks
{
  "list": {},
  "cancel": {},
  "requests": {
    "tools": {
      "call": {}
    },
    "prompts": {
      "get": {}
    },
    "resources": {
      "read": {}
    }
  }
}
tools
{
  "listChanged": true
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
jobsC

Workspace and task state. Actions: state, status, log, cancel.

seriesC

Stored series. Actions: search, get, add, update.

update fetches the named ids; with quantity it instead discovers the objects of that driver quantity and fetches their series.

corpusC

The scenario corpus. Actions: search, get, list, add, index, fetch.

search and get read the index as it was on as_of; list shows what the manifest holds; add registers a file downloaded by hand under its key. index and fetch are tasks on the corpus resource.

scenarioB

Stored scenarios. Actions: list, get, save, validate.

save checks the contract and writes scenarios/<id>.json; validate loads the macro and satellite versions the file names and names every problem that keeps the scenario from being computed, including corpus fragments not visible on the calculation day when the index exists.

runB

Runs on the paths of a scenario. Actions: start, status, result, list.

start draws the scenario's paths and calculates a portfolio's positions — or the positions given in the call — on them, as a job on the runs resource; status reports that job and what the run has written, result the per-position results, the conditional forecast of the targets (forecast.json: P5/P50/P95 of every target driver by month, in its own unit; every driver when targets is omitted) and the report of a portfolio run, list every run of the workspace. base_currency is the base of positions; a portfolio brings its own.

portfolioB

Stored portfolios. Actions: list, get, save, delete, check, exposure.

save checks the contract and writes portfolios/<id>.json; check names every position the scenario's macro and satellite versions cannot serve, with the reason of a driver the out-of-sample check rejected; exposure values the positions at the calculation month of the scenario's macro version.

modelB

Model versions and plugins. kind: macro, satellites, catalog.

The episode check of the models is backtest (python -m mst.metrics). Actions for macro: list, describe, estimate, backtest; for satellites: list, describe, estimate; for catalog: list, the installed model plugins. model_id names the macro model to estimate or backtest — the only one installed when omitted; assignment maps a quantity to the satellite model that estimates it in an estimate of the satellites, overriding the model that declares it. version is the version hash describe reads and the macro version the satellites of an estimate are built on; list takes no version.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

B3.2/5.0

Scored across 7 tools

Disambiguation4/5

The seven tools map to clearly distinct resources: jobs, corpus, portfolio, run, model, scenario, and series. Minor overlap exists around job/run status and corpus/series read actions, but the descriptions generally disambiguate them.

Naming Consistency4/5

Tool names consistently use lowercase resource nouns with actions scoped inside each tool, which is a predictable pattern. The only noticeable inconsistency is plural 'jobs' versus mostly singular names like 'run' and 'portfolio'.

Tool Count5/5

Seven tools is well scoped for a macro stress-testing server. Resource-level multiplexing exposes many operations without creating excessive tool sprawl.

Completeness4/5

The surface covers core workflows: corpus indexing/fetching, scenario validation/saving, portfolio checking/exposure, run execution/results, model estimation/backtesting, and job control. Minor gaps exist, such as scenario deletion, series deletion/listing, and explicit run deletion though some may be covered indirectly.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources