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Glama

Wolfram Alpha

by io.github.pipeworx-io

Wolfram Compute

wolfram_compute
Read-onlyIdempotent

Evaluate Wolfram Language code in a real Wolfram kernel — symbolic math (Integrate, Solve, DSolve, Simplify, Series, Limit), exact arithmetic, matrix algebra, number theory, unit conversion, and any Wolfram Language expression. Answers "integrate x^2 sin x", "solve this equation symbolically", "eigenvalues of this matrix". Give actual Wolfram Language code. Example: wolfram_compute({ code: "Integrate[x^2 Sin[x], x]" })

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
codeYesWolfram Language code to evaluate, e.g. "Solve[x^2 + 3x - 4 == 0, x]" or "Eigenvalues[{{1,2},{3,4}}]"
time_constraint_secondsNoEvaluation time limit in seconds, 1-60 (default 30)

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "code": "Integrate[x^2 Sin[x], x]"
+  },
+  {
+    "code": "Solve[x^2 + 3*x - 4 == 0, x]"
+  }
+]
  2. Added

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint=false, covering the safety profile. The description adds the key behavioral detail that it runs in a 'real Wolfram kernel,' indicating actual computation rather than a mock or LLM guess. This goes beyond annotations, so a 4 is appropriate.

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?

The description is efficient with three sentences: purpose, example queries, and an explicit input example. It is front-loaded and each clause adds value, though slightly dense. No filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

The tool has no output schema, so the description should explain return behavior, but it does not mention what the kernel returns (e.g., result, error messages) or how time_constraint_seconds affects execution. It covers common use cases well but omits these details, leaving gaps for a computational tool.

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% for both parameters, so the schema already documents code and time_constraint_seconds. The description adds an explicit call example and reinforces that 'code' must be actual Wolfram Language code, but this is marginal beyond the schema. Baseline 3 is correct.

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 evaluates Wolfram Language code in a real Wolfram kernel, with specific verb ('Evaluate') and resource ('Wolfram Language code'). It lists concrete capabilities (symbolic math, exact arithmetic, matrix algebra, unit conversion) and example queries, making it unmistakable versus the unrelated sibling tools.

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 usage context by giving natural-language examples ('integrate x^2 sin x', 'solve this equation symbolically') and instructs the user to 'Give actual Wolfram Language code.' It does not explicitly exclude alternatives or mention when not to use it, but given no sibling tool overlaps, this is sufficient.

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.

Resources