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newton

by io.github.pipeworx-io

Simplify

simplify
Read-onlyIdempotent

Reduce a mathematical expression to its simplest form. Input algebraic notation (e.g., "2^2+2(2)"). Returns simplified result.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
expressionYesMathematical expression to simplify (e.g., "2^2+2(2)", "x^2+2x+1")

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesThe input expression
resultYesSimplified mathematical expression or null
operationYesOperation name (simplify)

Schema Changelog

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

  1. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "input": {
+      "description": "The input expression",
+      "type": "string"
+    },
+    "operation": {
+      "description": "Operation name (simplify)",
+      "type": "string"
+    },
+    "result": {
+      "description": "Simplified mathematical expression or null",
+      "type": [
+        "string",
+        "null"
+      ]
+    }
+  },
+  "required": [
+    "operation",
+    "input",
+    "result"
+  ],
+  "type": "object"
+}
  2. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "expression": "2^2+2(2)"
+  },
+  {
+    "expression": "x^2+2x+1"
+  }
+]
  3. First observed

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the safety profile is known. The description adds that it returns a simplified result, which is consistent but not surprising. It also hints at supporting algebraic notation with implicit multiplication (e.g., '2(2)'), which is a mild behavioral detail, but overall limited added value beyond 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?

The description is two sentences, front-loaded with the primary purpose, and includes a concrete example. There is no redundant or extraneous text; every sentence earns its place.

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?

The tool is simple with one parameter, annotations cover safety, and an output schema exists (though not shown). The description adequately conveys the tool's function and expected input format, leaving no significant gaps for the agent to misinvoke.

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 the parameter 'expression' fully described and an example provided. The description repeats the same example and adds 'algebraic notation' as a general term, but does not significantly expand beyond the schema's own description. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states that the tool reduces a mathematical expression to its simplest form, using the specific verb 'reduce' and resource 'mathematical expression'. It provides an example of input notation, making the purpose clear. However, it does not explicitly differentiate from sibling tools like 'factor' or 'derive', so it lacks explicit sibling differentiation.

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

Usage Guidelines2/5

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

The description does not specify when to use this tool versus alternatives. It does not mention any exclusions, prerequisites, or compare with sibling tools such as 'factor' or 'derive'. There is no guidance on context or selection criteria.

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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TDQS

A3.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, such as multiple ways to get entity information (ask_pipeworx, ask_pipeworx_grounded, deep_research, entity_profile, compare_entities) and numerous prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.). Despite detailed descriptions, the boundaries are unclear, making it difficult for an agent to distinguish between them.

Naming Consistency4/5

Tool names mostly follow a snake_case convention and are generally descriptive. Minor inconsistencies exist, such as 'discover_tools' vs. 'scan_competitor_ai_presence' and a few single-word verbs like 'derive' and 'remember'. Overall, the pattern is predictable and readable.

Tool Count2/5

With 34 tools, the server is heavy for a single server. The scope is very broad, covering math, memory, data retrieval, prediction markets, and more. While each tool has a specific purpose, the high count suggests a lack of focus and could overwhelm an agent.

Completeness4/5

The server offers extensive coverage for data retrieval, entity lookup, comparison, research, and prediction markets. Minor gaps exist, such as missing advanced math operations (e.g., solving equations) and some niche data sources, but the core workflows are well-covered with tools like ask_pipeworx and deep_research.

Resources