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Glama

VARRD — Statistically Validated Trading Edges + AI Research Engine

by io.github.augiemazza

get_hypothesis

Read-onlyIdempotent

Get full detail for a specific hypothesis/strategy. Returns formula, entry/exit rules, direction, performance metrics (win rate, Sharpe, profit factor, max drawdown), version history, and trade levels. Everything an agent needs to understand and act on a strategy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
hypothesis_idYesThe hypothesis ID — from varrd_edges (any depth), search, or scan results.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNo
formulaNo
win_rateNo
directionNo
hypothesis_idNo
horizon_resultsNo

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • changedInput schema / properties / hypothesis_id / description
      Previous value: -"The hypothesis ID (from search or scan results)."New value: +"The hypothesis ID — from varrd_edges (any depth), search, or scan results."
  2. Changed17 schema fields changed
    • removedOutput schema / properties / beats_market
      Removed value: -{
-  "type": "boolean"
-}
    • removedOutput schema / properties / created_at
      Removed value: -{
-  "type": "string"
-}
    • removedOutput schema / properties / direction / enum
      Removed value: -[
-  "LONG",
-  "SHORT"
-]
    • removedOutput schema / properties / entry_offset
      Removed value: -{
-  "type": "integer"
-}
    • removedOutput schema / properties / ev_per_trade
      Removed value: -{
-  "type": "number"
-}
    • removedOutput schema / properties / explanation
      Removed value: -{
-  "type": "string"
-}
    • removedOutput schema / properties / formula / description
      Removed value: -"DSL boolean expression"
    • removedOutput schema / properties / has_edge
      Removed value: -{
-  "type": "boolean"
-}
    • removedOutput schema / properties / market
      Removed value: -{
-  "type": "string"
-}
    • removedOutput schema / properties / performance
      Removed value: -{
-  "description": "Precomputed analytics (if available from scanner)",
-  "type": "object"
-}
    • removedOutput schema / properties / profit_factor
      Removed value: -{
-  "type": "number"
-}
    • removedOutput schema / properties / selected_horizon
      Removed value: -{
-  "type": "integer"
-}
    • removedOutput schema / properties / setup_code
      Removed value: -{
-  "description": "Full DSL code that computes the pattern",
-  "type": "string"
-}
    • removedOutput schema / properties / sharpe
      Removed value: -{
-  "type": "number"
-}
    • removedOutput schema / properties / test_type
      Removed value: -{
-  "enum": [
-    "event_study",
-    "backtest"
-  ],
-  "type": "string"
-}
    • removedOutput schema / properties / versions
      Removed value: -{
-  "type": "array"
-}
    • removedOutput schema / required
      Removed value: -[
-  "hypothesis_id",
-  "name"
-]
  3. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "beats_market": {
+      "type": "boolean"
+    },
+    "created_at": {
+      "type": "string"
+    },
+    "direction": {
+      "enum": [
+        "LONG",
+        "SHORT"
+      ],
+      "type": "string"
+    },
+    "entry_offset": {
+      "type": "integer"
+    },
+    "ev_per_trade": {
+      "type": "number"
+    },
+    "explanation": {
+      "type": "string"
+    },
+    "formula": {
+      "description": "DSL boolean expression",
+      "type": "string"
+    },
+    "has_edge": {
+      "type": "boolean"
+    },
+    "horizon_results": {
+      "type": "array"
+    },
+    "hypothesis_id": {
+      "type": "string"
+    },
+    "market": {
+      "type": "string"
+    },
+    "name": {
+      "type": "string"
+    },
+    "performance": {
+      "description": "Precomputed analytics (if available from scanner)",
+      "type": "object"
+    },
+    "profit_factor": {
+      "type": "number"
+    },
+    "selected_horizon": {
+      "type": "integer"
+    },
+    "setup_code": {
+      "description": "Full DSL code that computes the pattern",
+      "type": "string"
+    },
+    "sharpe": {
+      "type": "number"
+    },
+    "test_type": {
+      "enum": [
+        "event_study",
+        "backtest"
+      ],
+      "type": "string"
+    },
+    "versions": {
+      "type": "array"
+    },
+    "win_rate": {
+      "type": "number"
+    }
+  },
+  "required": [
+    "hypothesis_id",
+    "name"
+  ],
+  "type": "object"
+}
  4. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations cover read-only, idempotent, non-destructive behavior. The description adds meaningful context beyond that by enumerating exactly what is returned—formula, entry/exit rules, direction, performance metrics, version history, trade levels. No contradiction with annotations.

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?

Three short sentences with the action front-loaded in the first sentence, a concise enumeration of return contents in the second, and a purpose statement in the third. No wasted words or repetition.

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?

For a single-parameter read-only tool with full annotations and an output schema, the description is complete. It explains what the tool returns, when to use it, and where the ID comes from. Nothing critical is missing for an agent to select and invoke it correctly.

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%; the hypothesis_id parameter is well described with its source (varrd_edges, search, scan results). The description does not add parameter-specific guidance beyond referring to a specific hypothesis/strategy, so baseline 3 is appropriate.

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 a specific verb and resource: getting full detail for a specific hypothesis/strategy. It lists concrete return fields and distinguishes itself from sibling tools like search or varrd_edges by focusing on retrieval of complete details for a known ID.

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 clear context: use when you already have a hypothesis_id from varrd_edges, search, or scan results and need full strategy details to understand and act. It does not explicitly name alternatives or when-not-to-use, but the intended context is unambiguous.

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

All VARRD — Statistically Validated Trading Edges + AI Research Engine toolsInspect
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TDQS

A4.4/5.0
Disambiguation4/5

Most tools have distinct purposes, but 'autonomous_varrd_ai' and 'varrd_ai' could cause confusion despite clear descriptions. 'varrd_edges' vs 'varrd_ai' are distinct. Overall, minor overlap but manageable.

Naming Consistency3/5

Names mix snake_case verbs and noun phrases, and the 'varrd_' prefix is inconsistently applied. 'autonomous_varrd_ai' deviates from the verb_noun pattern. Still readable.

Tool Count5/5

9 tools is well-scoped for the server's purpose—covers research, credit management, results browsing, and session control without bloat.

Completeness4/5

Covers core workflows: hypothesis generation, testing, browsing live edges, and managing credits. Missing explicit update/delete capabilities for hypotheses, but overall surface is sufficient.

Resources