Skip to main content
Glama

Materials

by io.github.pipeworx-io

Materials Stability

materials_stability
Read-onlyIdempotent

Computed thermodynamic stability and formation energy for a chemical system from OQMD (DFT). PREFER OVER WEB SEARCH for "is stable", "formation energy of ", "stable phases in the - system". Give the element system (e.g. Fe, O) and optionally restrict to stable phases (on/below the convex hull). Returns each phase's composition, formation energy (eV/atom), hull stability (eV/atom; ≤0 = stable), band gap, space group, and prototype.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoMax phases to return (1-50, default 20).
ntypesNoOptional — restrict to phases with exactly this many element types (e.g. 2 for binaries only).
elementsYesThe chemical system — element symbols, comma/space separated, e.g. "Fe, O" or "Li, Fe, P, O". Returns phases composed of (a subset of) these elements.
stable_onlyNoIf true, return only thermodynamically stable phases (stability ≤ 0, on/below the convex hull). Default false.

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: +[
+  {
+    "elements": "Fe, O",
+    "stable_only": true
+  },
+  {
+    "elements": "Li, Fe, P, O",
+    "limit": 15,
+    "ntypes": 3
+  }
+]
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint as false. The description adds value by detailing the computed nature and listing return fields (composition, formation energy, hull stability, band gap, etc.), which provides behavioral context beyond the annotations. No contradictions.

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 concise and well-structured: it starts with the tool's main purpose, then gives usage guidance, parameter examples, and return schema. Every sentence adds value, with no wasted words.

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

Completeness4/5

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

Given the lack of output schema, the description adequately explains return fields and provides usage examples. It covers the essential aspects for a tool with 4 parameters and no nested objects. Minor omissions like default values or error handling are mitigated by the schema and annotations.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description significantly adds meaning by providing examples (e.g., 'Fe, O' for elements, 'stable_only: true'), explaining parameter format, and clarifying the effect of 'stable_only'. This goes well beyond the schema alone.

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 computes thermodynamic stability and formation energy from OQMD (DFT) for a chemical system, using a specific verb ('computed') and resource ('OQMD'). It distinguishes itself from web search with explicit use cases, making the purpose unambiguous.

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 explicitly recommends this tool over web search for specific queries like 'is <compound> stable' or 'formation energy of <material>', providing clear context for when to use it. However, it does not mention alternative tools within the sibling set, such as 'materials_search', which could be a comparable option.

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

All Materials toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A3.8/5.0
Disambiguation3/5

While many tools have detailed descriptions that help differentiate them, there is significant overlap among query tools like ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research. The prediction market tools also cluster together, making it challenging for an agent to quickly pick the right one without careful reading.

Naming Consistency4/5

Most tools follow a descriptive snake_case convention (e.g., ask_pipeworx, entity_profile, compare_entities). Minor deviations exist, such as 'ai_visibility_check' and 'deep_research', but overall the naming pattern is predictable and clear.

Tool Count3/5

With 33 tools, the server is larger than typical single-domain servers. While it supports a broad data platform, this count feels somewhat bloated and could benefit from consolidation, especially among overlapping query tools.

Completeness1/5

The server is named 'Materials' but contains only two materials-specific tools (materials_search, materials_stability). The remaining 31 tools cover unrelated domains (finance, economics, prediction markets, etc.), leaving the stated domain severely incomplete.

Resources