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Glama

Wolfram Alpha

by io.github.pipeworx-io

Short Answer

short_answer
Read-onlyIdempotent

Get a single terse plain-text answer from Wolfram Alpha. Best for: arithmetic, unit conversion, "what is X", "how many Y in Z", factual lookups (planet diameter, country GDP, element atomic weight, current time in Tokyo). Returns one string.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNatural-language query
unitsNometric | imperial (default metric)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe query string that was sent to Wolfram Alpha
answerYesThe terse plain-text answer, or null if not understood
messageNoError or status message if answer is null

Schema Changelog

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

  1. Changed2 schema fields changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "what is the capital of France"
+  },
+  {
+    "query": "how many kilometers in 50 miles",
+    "units": "metric"
+  },
+  {
+    "query": "2^10",
+    "units": "imperial"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "answer": {
+      "description": "The terse plain-text answer, or null if not understood",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "message": {
+      "description": "Error or status message if answer is null",
+      "type": "string"
+    },
+    "query": {
+      "description": "The query string that was sent to Wolfram Alpha",
+      "type": "string"
+    }
+  },
+  "required": [
+    "query",
+    "answer"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A3.9/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint, and non-destructive behavior. The description adds that the answer is 'terse' and returns 'one string', which is some behavioral context, but does not go beyond what annotations and output schema imply. No contradiction.

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 main purpose, and every sentence provides value. The 'Returns one string' is a clear and useful outcome statement.

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?

Given the tool's simplicity, full schema coverage, and the presence of an output schema, the description adequately covers purpose, use cases, and behavioral expectations. No missing information that would impede correct usage.

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%, so parameters are fully documented. The description adds general use-case context (e.g., 'unit conversion' implies the units parameter) but no new parameter-specific semantics, aligning with the baseline for high schema coverage.

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 'Get a single terse plain-text answer from Wolfram Alpha' and lists example use cases, making the purpose obvious. However, it does not explicitly name or differentiate from sibling tools like wolfram_compute or full_query, so it falls short of a 5.

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 'Best for' list provides clear context for when to use the tool (arithmetic, unit conversion, factual lookups). However, it lacks explicit 'when not to use' guidance or references to alternative tools, which would earn a 5.

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

A3.7/5.0
Disambiguation2/5

Several tools are near-indistinguishable: ask_pipeworx and ask_pipeworx_beta are currently identical, and ask_pipeworx/ask_pipeworx_grounded/deep_research have overlapping routing behavior. discover_tools vs suggest_questions and ai_visibility_check vs scan_competitor_ai_presence also create boundary ambiguity, making misselection likely for agents.

Naming Consistency2/5

Names mix bare verbs (recall, remember, forget), brand-prefixed nouns (pipeworx_trending, polymarket_edges), and descriptive phrases (generate_llms_txt, scan_competitor_ai_presence). There is no consistent verb_noun or prefix convention, and the server name 'Wolfram Alpha' does not match the dominant pipeworx_/polymarket_ naming.

Tool Count2/5

At 34 tools, the set exceeds the 25+ threshold and feels heavy even for a broad data platform. Several unrelated add-ons (memory trio, ai_visibility, generate_llms_txt, scan_dependency) could live in separate servers, contributing to bloat and diluting the core purpose.

Completeness3/5

The data-research and prediction-market surfaces are fairly thorough (routing, grounded verification, deep research, entity resolution, comparisons, subscriptions), but the Wolfram Alpha core is thin—only short_answer, full_query, and wolfram_compute—with no step-by-step solutions, units catalog, or history. The mismatched server name and unrelated tools indicate an incoherent scope with notable gaps.

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