VARRD — Statistically Validated Trading Edges + AI Research Engine

search

Read-onlyIdempotent

Search your saved hypotheses by keyword or natural language query. Returns matching strategies ranked by relevance, with key stats (win rate, Sharpe, edge status). Use this to find strategies you've already validated.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "search"
  }
}

Input Schema

Name	Required	Description
`limit`	No	Max results to return.
`query`	Yes	Search query — keywords or natural language (e.g. 'momentum strategies', 'RSI oversold').
`market`	No	Optional market filter.

Output Schema

Name	Required	Description
`query`	No
`method`	No	Search method: embedding or keyword
`results`	No	Matching strategies with win rate, Sharpe, similarity

Schema Changelog

Changes observed during successful MCP inspections.

Changed5 schema fields changedMay 5, 2026

addedOutput schema / properties / method / description

Added value: +"Search method: embedding or keyword"

removedOutput schema / properties / method / enum

Removed value: -[
-  "embedding",
-  "keyword"
-]

addedOutput schema / properties / results / description

Added value: +"Matching strategies with win rate, Sharpe, similarity"

removedOutput schema / properties / results / items

Removed value: -{
-  "properties": {
-    "direction": {
-      "type": "string"
-    },
-    "has_edge": {
-      "type": "boolean"
-    },
-    "hypothesis_id": {
-      "type": "string"
-    },
-    "market": {
-      "type": "string"
-    },
-    "name": {
-      "type": "string"
-    },
-    "similarity": {
-      "description": "Cosine similarity (embedding search only)",
-      "type": "number"
-    },
-    "win_rate": {
-      "type": "number"
-    }
-  },
-  "type": "object"
-}

removedOutput schema / required

Removed value: -[
-  "results",
-  "query"
-]

Changed1 schema field changedMay 4, 2026

changedOutput schema / (root)

Previous value: -nullNew value: +{
+  "properties": {
+    "method": {
+      "enum": [
+        "embedding",
+        "keyword"
+      ],
+      "type": "string"
+    },
+    "query": {
+      "type": "string"
+    },
+    "results": {
+      "items": {
+        "properties": {
+          "direction": {
+            "type": "string"
+          },
+          "has_edge": {
+            "type": "boolean"
+          },
+          "hypothesis_id": {
+            "type": "string"
+          },
+          "market": {
+            "type": "string"
+          },
+          "name": {
+            "type": "string"
+          },
+          "similarity": {
+            "description": "Cosine similarity (embedding search only)",
+            "type": "number"
+          },
+          "win_rate": {
+            "type": "number"
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    }
+  },
+  "required": [
+    "results",
+    "query"
+  ],
+  "type": "object"
+}

First observedMar 1, 2026

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is known. The description adds meaningful behavioral context: it searches only saved hypotheses (scope) and returns ranked results with key stats (win rate, Sharpe, edge status), which is not in annotations. It does not mention pagination or rate limits, but it adds enough beyond annotations to warrant a 4.

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?

The description is two sentences, front-loaded with the core action, and every sentence earns its place. It states what the tool does, what it returns, and when to use it without any redundant filler.

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?

With strong annotations, a fully described schema, and an output schema present, the description delivers the essential purpose, usage, and return behavior. For a simple search tool with one required parameter, this is complete and leaves no obvious gaps.

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%, with each parameter (query, limit, market) already well-described. The description reinforces the query parameter's purpose ('keyword or natural language query') but does not add new syntactic details or clarify parameter behavior beyond the schema, so it sits at the baseline of 3.

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?

The description clearly states the tool's function: 'Search your saved hypotheses by keyword or natural language query.' It specifies the resource (saved hypotheses), the action (search), and the method (keyword or natural language). It also mentions the output ('returns matching strategies ranked by relevance'), which distinguishes it from siblings like get_hypothesis.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit usage context: 'Use this to find strategies you've already validated.' This tells the agent when to invoke this tool (for searching existing validated strategies) but does not explicitly name alternatives or exclusion cases, so it falls short of the highest bar.

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

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