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Glama

openalex

by io.github.pipeworx-io

Get Concept

get_concept
Read-onlyIdempotent

Look up research fields or topics by name. Returns concept description, publication count, related concepts, and parent concepts in the academic hierarchy.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesConcept name to look up (e.g., "deep learning")
_apiKeyNoOpenAlex API key. Optional — Pipeworx supplies one; pass your own to bill your account instead.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoOpenAlex concept ID
foundYesWhether a matching concept was found
levelNoConcept hierarchy level
queryYesThe concept query searched
ancestorsNoParent concepts in hierarchy
descriptionNoConcept description
works_countNoNumber of works in this field
display_nameNoConcept display name
cited_by_countNoTotal citations in this field
related_conceptsNoRelated concepts (up to 10)

Schema Changelog

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

  1. Changed1 schema field changed
    • addedInput schema / properties / _apiKey
      Added value: +{
+  "description": "OpenAlex API key. Optional — Pipeworx supplies one; pass your own to bill your account instead.",
+  "type": "string"
+}
  2. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "dependentRequired": {
+    "found": [
+      "id",
+      "display_name",
+      "level",
+      "description",
+      "works_count",
+      "cited_by_count",
+      "ancestors",
+      "related_concepts"
+    ]
+  },
+  "properties": {
+    "ancestors": {
+      "description": "Parent concepts in hierarchy",
+      "items": {
+        "properties": {
+          "level": {
+            "description": "Ancestor hierarchy level",
+            "type": "number"
+          },
+          "name": {
+            "description": "Ancestor concept name",
+            "type": "string"
+          }
+        },
+        "required": [
+          "name",
+          "level"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "cited_by_count": {
+      "description": "Total citations in this field",
+      "type": "number"
+    },
+    "description": {
+      "description": "Concept description",
+      "type": [
+        "string",
+        "null"
+      ]
+    },
+    "display_name": {
+      "description": "Concept display name",
+      "type": "string"
+    },
+    "found": {
+      "description": "Whether a matching concept was found",
+      "type": "boolean"
+    },
+    "id": {
+      "description": "OpenAlex concept ID",
+      "type": "string"
+    },
+    "level": {
+      "description": "Concept hierarchy level",
+      "type": "number"
+    },
+    "query": {
+      "description": "The concept query searched",
+      "type": "string"
+    },
+    "related_concepts": {
+      "description": "Related concepts (up to 10)",
+      "items": {
+        "properties": {
+          "level": {
+            "description": "Related concept hierarchy level",
+            "type": "number"
+          },
+          "name": {
+            "description": "Related concept name",
+            "type": "string"
+          },
+          "score": {
+            "description": "Relatedness score",
+            "type": "number"
+          }
+        },
+        "required": [
+          "name",
+          "level",
+          "score"
+        ],
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "works_count": {
+      "description": "Number of works in this field",
+      "type": "number"
+    }
+  },
+  "required": [
+    "query",
+    "found"
+  ],
+  "type": "object"
+}
  3. Changed1 schema field changed
    • addedInput schema / examples
      Added value: +[
+  {
+    "query": "deep learning"
+  }
+]
  4. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about the return payload (publication count, related/parent concepts), going beyond the annotations. It does not contradict annotations and gives a clear picture of expected behavior for a simple lookup.

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 concise sentences, front-loaded with the core action, and every clause adds information. No wasted words or redundancy with structured fields.

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?

For a simple read-only lookup with an output schema and strong annotations, the description is nearly complete. It covers what the tool does and what it returns. A minor gap is lack of guidance on handling ambiguous or non-existent concept names, but overall it is sufficient.

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 'query' and '_apiKey' already well described. The description's phrase 'by name' adds minimal value beyond the schema's 'Concept name to look up'. It does not introduce new constraints or deeper parameter semantics.

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's function: 'Look up research fields or topics by name.' It specifies the resource (concepts in the academic hierarchy) and what is returned (description, publication count, related concepts, parent concepts), which distinguishes it from sibling tools like get_scholarly_work or search_works.

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

Usage Guidelines3/5

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

The description implies use for concept lookup by name but provides no explicit guidance on when to prefer this tool over alternatives like search_works or entity_profile. There are no exclusions or alternative recommendations, so it is only minimally viable.

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

A3.5/5.0
Disambiguation1/5

Several tools are effectively indistinguishable or near-duplicates: ask_pipeworx_beta is explicitly an identical copy of ask_pipeworx with no active experimental changes, and ask_pipeworx_grounded is a variant of the same router. discover_tools, suggest_questions, deep_research, and the ask_pipeworx family also heavily overlap as discovery/answer surfaces, while bet_research, polymarket_edges, polymarket_arbitrage, and polymarket_fill_risk create a dense prediction-market cluster with fuzzy boundaries.

Naming Consistency3/5

Everything is snake_case and many names follow a readable verb_noun pattern (search_works, resolve_entity, validate_claim, suggest_questions), but conventions are mixed: noun-first domain-prefixed names (polymarket_edges, pipeworx_feedback, pipeworx_trending), bare verbs (remember, forget, recall), and adjective_noun names (deep_research, recent_changes) coexist. The inconsistency is not chaotic, but it is not a unified scheme.

Tool Count2/5

36 tools is well over the 25-tool threshold for a heavy toolset, and the count is inflated by many tangential concerns: memory, subscriptions, feedback, trending, npm dependency scanning, AI visibility, and llms.txt generation. Only a small subset actually serves the stated OpenAlex/scholarly purpose, so the size feels bloated rather than well-scoped.

Completeness2/5

For a server named openalex, the scholarly surface is incomplete: works support search and fetch, but authors and institutions only support search with no get-by-ID, concepts support get but no search, and major OpenAlex resource types like sources, publishers, funders, and topics are absent. The many non-OpenAlex tools do not fill these gaps and instead dilute the domain coverage.

Resources