AlternativeSoft MCP lets any AI assistant run AlternativeSoft analytics - directly, securely, in plain language. Bring AlternativeSoft into every AI workflow: the same trusted engine, the same data, with a natural-language front door.
| Fund | Strategy | Sharpe | Max DD |
|---|---|---|---|
| FGC Multi Strategy | Multi-Strategy | 1.88 | -16.7 |
| ECM Enhanced Global | Multi-Strategy | 1.70 | -14.9 |
| NCM Absolute Alpha | Multi-Strategy | 0.67 | -27.4 |
Get the full picture of AlternativeSoft MCP - how a question asked in plain language becomes live analytics, computed on your own data, and what that means for your team's workflow.
The product, the use cases and the security model - in one PDF you can share with your team.
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AlternativeSoft MCP meets them there - turning natural-language questions into real analytics, run on your own data.
Future-proofs AlternativeSoft against the rapid shift to AI assistants and agentic workflows - without changing the engine your team already trusts.
Ask for a Sharpe ratio, a peer comparison or a drawdown series - no menus, no exports. Just type the question and get a reasoned, data-backed answer.
MCP (Model Context Protocol) is an open standard that lets AI assistants call external tools. AlternativeSoft exposes its analytics as MCP tools - so the assistant can fetch funds, build comparisons and compute statistics on demand.
“Compare these three funds on Sharpe and max drawdown.”
Maps the request to the right analytics tools and parameters.
Runs the calculation on your database and returns the result.
No lock-in. AlternativeSoft MCP plugs into any assistant that supports MCP servers - and more are being added across the industry.
Choose the deployment that fits each assistant. Web-based assistants consume Server-Hosted MCP; desktop and developer tools can make use of the MCP locally.
Supports browser-based assistants, desktop and developer tools that connect to a hosted endpoint.
Suitable for desktop and developer tools that launch locally - ideal for AlternativeSoft desktop users.
From fund discovery to due-diligence prep - the same questions your analysts ask today, answered live and in plain English.
“Find funds in my Long/Short peer group with the best 3-year Sharpe.”
“Compare Fund A, B and C on return, volatility and max drawdown.”
“Give me the rolling 12-month beta of this fund versus its benchmark.”
“Which funds sit in my Growth portfolio, and what are their weights?”
“Does this fund belong to my Approved-Managers group?”
“Summarise key risk stats for these managers ahead of the IC meeting.”
Real sessions in Claude Desktop. The analyst asks in plain language; AlternativeSoft computes over MCP and renders the result.
“Get me the list of funds in my sample hedge fund portfolio and some attributes and stats for them.”
Claude loads the AlternativeSoft tools, resolves the correct statistic names, then calls GetGroupAssets on your engine.
Eight funds returned with strategy, currency, AUM, return, volatility, Sharpe, Sortino and max drawdown - as an interactive table.
Claude highlights the best risk-adjusted names and the highest-volatility outliers - analysis, not just data.
| Fund | Strategy | Sharpe | Sortino | Max DD |
|---|---|---|---|---|
| FGC Multi Strategy | Multi-Strategy | 1.88 | 4.39 | -16.7 |
| ECM Enhanced Global | Multi-Strategy | 1.70 | 4.13 | -14.9 |
| NCM Absolute Alpha | Multi-Strategy | 0.67 | 1.23 | -27.4 |
| ValueTech High Return | Directional | 1.14 | 2.73 | -18.5 |
“Pull Fundsmith Equity’s latest performance and risk stats and write a two-paragraph assessment I can use in the quarterly review.”
Claude fetches the supported statistics for the GBP Acc share class - returns, volatility and Sharpe across 1/3/5-year windows - directly from your data.
A structured Performance and Risk narrative, grounded in the figures it just pulled - report-ready prose alongside live data.
A few habits keep responses fast, accurate and economical on tokens.
Name the exact funds, group, statistics and date window in one prompt. Specifics avoid back-and-forth and wasted calls.
Request only the statistics and fields you need. Narrow scope returns smaller payloads - faster and cheaper.
Point the AI at an existing peer group or portfolio instead of long fund lists. One reference replaces many names.
For time-series data, daily over decades is heavy; monthly is usually enough for comparisons.
Start broad to shortlist, then ask follow-ups on the survivors - rather than pulling everything at once.
Let the AI resolve exact asset names first. Correct names prevent failed lookups and retries.