Skip to main content
Glama
ferinator

Bullrun

by ferinator
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Remote

Screen the risk board

screen_risk_board
Read-only

Rank ~9,000 scored stocks by composite risk or one mechanism dial, then filter by risk band, sector, country, and dial coverage to surface strained or calm companies.

Instructions

Rank Bullrun's scored stock universe (~9,000 companies) by company risk: the composite risk score or any one mechanism dial (market expectation, operating pressure, cash conversion, leverage, competitive position, concentration), filtered by band, sector, country and dial coverage. Returns ticker, score, band and its meaning, direction and move since the prior reading, and the driving input per row, plus the universe band distribution and how many companies changed band at the last recompute. Use for "which large caps have stretched leverage", "most concentrated supply chains in semiconductors", "calmest German industrials", or "how risky is tech vs utilities" (includeSlices). Drill into a row with get_company_risk. A ranking of readings, not of companies to avoid. Read-only.

Input Schema

NameRequiredDescriptionDefault
bandsNoKeep only these bands, e.g. ["STRETCHED","ACUTE"] for the most strained readings or ["CONTAINED"] for the calmest.
limitNo
offsetNo
sectorNoExact sector, case-insensitive (e.g. "Technology", "Industrials"). Set includeSlices=true to list the sectors the board covers.
sortByNomarket_cap = largest companies first (default; a pure score sort surfaces micro-caps); score_desc = most strained first; score_asc = calmest first; worsened / improved = biggest move since the prior reading; coverage = most dials lit; name.market_cap
countryNoExact country, case-insensitive (e.g. "United States", "Germany").
mechanismNoRank by the composite (OVERALL) or by one mechanism dial: MARKET (drawdown, volatility, liquidity), OPERATING (margin/cost pressure), CASH_CONVERSION (cash vs earnings, capex), LEVERAGE (debt, interest cover, liquidity; CET1 for banks), COMPETITIVE (R&D, margins vs peers, technology transition), CONCENTRATION (supplier, customer, geographic).OVERALL
minDialsLitNoCoverage floor: only companies with at least this many of the six dials measured. 4+ avoids thinly-measured composites.
includeSlicesNoAlso return mean composite risk by sector and by country across the scored universe.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv0.7.0

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint/destructiveHint=false, and the description adds real value beyond that: it states what each row returns (ticker, score, band and meaning, direction, move since prior reading, driving input) plus universe-level band distribution and band-change counts. It does not discuss pagination caps or rate limits, so it stops short of fully exhaustive.

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 the core action and universe, then filters, then return shape and use cases. Dense but nearly every clause earns its place; the single long paragraph is slightly run-on compared with a bulleted structure.

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?

No output schema exists, yet the description fully covers the return payload (per-row fields, band distribution, band-change count) and supplies the interpretive caveat. For a 9-parameter, all-optional screening tool, an agent has everything needed to call and read it correctly.

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 78%, so the schema carries most parameter docs, but the description still adds meaning: it groups the filter dimensions (band, sector, country, dial coverage) and explains the purpose of includeSlices for sector/country comparison. The mechanism dials are expanded with business meaning that reinforces the enum.

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 — rank the ~9,000-company scored universe by composite risk or a single mechanism dial — and enumerates the filters. It is clearly distinct from sibling screen_stocks (equity screening) and get_company_risk (single-row drill-down).

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?

Provides four concrete example queries mapping to real use cases, explains when to use includeSlices ("how risky is tech vs utilities"), and explicitly routes row-level inspection to get_company_risk. It also bounds interpretation: "A ranking of readings, not of companies to avoid."

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