quantifyme

Find a Matching Strategy

find_strategy

Read-onlyIdempotent

Find an existing PROVEN strategy that matches a plain-English idea, so you can offer the user a choice — deploy the existing one, or generate a fresh custom one. Mirrors the quantifyme.ai landing experience: "Found by @ (WR/PF) — Use it / Generate fresh".

CALL THIS FIRST when a user describes a strategy idea. Then present the match
(if any) and ASK which they want:
  • Use it          → one_shot(community_id=<match.community_id>) — deploys the
                      exact proven strategy (free, no generation).
  • Generate fresh  → one_shot(prompt="<their description>") — Claude writes a
                      brand-new custom strategy for them.
If there's no match, just offer to generate fresh.

Args:
  description: the user's strategy idea in plain English (e.g. "buy EURUSD
    15min when RSI < 30, sell when RSI > 70").
  symbol: optional pair to constrain the match (EURUSD, USDJPY, GBPUSD,
    USDCHF, USDCAD, AUDUSD, NZDUSD).
  timeframe: optional granularity to constrain the match (1min/5min/15min/1h).

Returns:
  dict with:
    - match: the best existing strategy, or null. When present:
        {community_id, title, username, wr, pf, ret, n_trades, symbol,
         timeframe}. Pass community_id to one_shot to deploy it unchanged.
    - description: echoed back — pass as one_shot(prompt=...) to generate fresh.
    - suggestion: a ready-to-show sentence offering the user the choice.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`symbol`	No
`timeframe`	No
`description`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedMay 24, 2026

TDQS

A4.7/5.0

Behavior4/5

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

Annotations already provide readOnlyHint, openWorldHint, idempotentHint. Description adds context about mirroring a landing experience, returning match or null, and a suggestion sentence. It also explains the output structure and how to use community_id. No contradictions.

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?

Well-structured with sections for purpose, usage, args, and returns. Somewhat long but all content is valuable for correct tool invocation. Could be slightly tighter, but it earns its length.

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?

Description fully covers the tool's role in the workflow, details all input parameters with examples, and describes the return dict comprehensively. Given the presence of an output schema and sibling tools, the description is complete and actionable.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Despite 0% schema description coverage, the description explains each parameter in detail: description with example, symbol with list of allowed values, timeframe with granularities. This adds significant meaning beyond the plain schema.

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?

Description clearly states 'Find an existing PROVEN strategy that matches a plain-English idea' and explains the resulting choice (deploy existing or generate fresh). It distinguishes itself from siblings like generate_strategy and one_shot by specifying its role as a discovery step.

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?

Explicitly instructs 'CALL THIS FIRST when a user describes a strategy idea' and provides detailed conditional logic: present match, ask user, then call one_shot with appropriate arguments depending on choice. Also clarifies behavior when no match is found.

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

All quantifyme tools Inspect

Try in Browser

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

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources

Need Help?