get_materialFetch a single OQMD material by its entry_id (e.g. 16525 = the Pbcn polymorph of Fe2O3). Returns formation energy (eV/atom), stability above hull, band gap, space group, prototype, cell volume, atom/element counts, ICSD id, and generic composition. Keyless.
| Name | Required | Description | Default |
|---|---|---|---|
| entry_id | Yes | OQMD entry_id, e.g. 16525 (Fe2O3, Pbcn). Get ids from search_materials results. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "entry_id": 16525
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, and non-destructive. The description adds useful behavioral context such as 'Keyless' (no auth needed) and enumerates the return fields, which goes 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.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, with the purpose front-loaded, followed by a compact list of return fields and a keyless note. Every word earns its place with zero waste.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter, read-only material fetch, the description covers what it returns, the key type, and authentication status. The absence of an output schema is mitigated by the enumerated return fields, making it complete for this low-complexity tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema already documents entry_id thoroughly with an example and source guidance. The description's example (16525) adds a tiny bit of context but largely duplicates the schema, so it meets the baseline of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool 'Fetch a single OQMD material by its entry_id' with a concrete example, distinguishing it from search-like siblings such as search_materials. The verb+resource+key is specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description and schema together imply usage context: you need an entry_id from previous search results, as stated in the parameter description ('Get ids from search_materials results'). However, it doesn't explicitly contrast with alternatives like stable_phases, so it stops short of full exclusion guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Many tools overlap in purpose: ask_pipeworx, ask_pipeworx_grounded, and deep_research all answer questions; multiple prediction market tools (polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker, polymarket_fill_risk) have subtle distinctions. An agent would struggle to choose correctly among these.
Tool names follow a consistent snake_case verb_noun pattern (e.g., list_subscriptions, generate_llms_txt). A few are noun phrases (stable_phases) but the style is uniform and predictable.
33 tools is high for a single server, especially given the mix of two unrelated domains (materials database and general data querying). Many prediction market tools could be consolidated, and the broad scope suggests over-engineering.
The materials data side covers search and retrieval adequately. The query side offers many capabilities but has redundant paths (e.g., multiple ways to ask questions) and gaps in editing or updating data. Overall coverage is mixed.