Skip to main content
Glama

Semanticscholar

by io.github.pipeworx-io

Search Papers

search_papers
Read-onlyIdempotent

Search 200M+ academic papers on Semantic Scholar by keyword or exact title. Returns titles, authors, year, venue, CITATION COUNTS, DOI, and open-access PDF links. PREFER for "how many citations does have", "citation count for ", "how cited is " — search the title and read citationCount off the match. Optionally filter by year range and field of study. Keyless.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
yearNoFilter by publication year or range, e.g. "2023" or "2020-2024".
limitNoMax results to return (default 10, max 25).
queryYesSearch query, e.g. "transformer attention mechanism" or "CRISPR gene editing".
fields_of_studyNoFilter by field of study, e.g. "Computer Science", "Medicine", "Biology", "Physics".

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query used
totalYesTotal number of matching papers
papersYesList of papers matching the search
returnedYesNumber of papers returned in this response

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: +[
+  {
+    "limit": 10,
+    "query": "transformer attention mechanism",
+    "year": "2023"
+  },
+  {
+    "limit": 20,
+    "query": "CRISPR gene editing",
+    "year": "2020-2024"
+  }
+]
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "papers": {
+      "description": "List of papers matching the search",
+      "items": {
+        "properties": {
+          "abstract": {
+            "description": "Paper abstract",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "authors": {
+            "description": "Paper authors",
+            "items": {
+              "properties": {
+                "id": {
+                  "description": "Author ID",
+                  "type": [
+                    "string",
+                    "null"
+                  ]
+                },
+                "name": {
+                  "description": "Author name",
+                  "type": [
+                    "string",
+                    "null"
+                  ]
+                }
+              },
+              "type": "object"
+            },
+            "type": "array"
+          },
+          "citation_count": {
+            "description": "Number of citations",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "doi": {
+            "description": "Digital Object Identifier",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "fields_of_study": {
+            "description": "Fields of study tags",
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          },
+          "influential_citations": {
+            "description": "Number of influential citations",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "is_open_access": {
+            "description": "Whether the paper is open access",
+            "type": [
+              "boolean",
+              "null"
+            ]
+          },
+          "journal": {
+            "description": "Journal name",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "open_access_pdf": {
+            "description": "URL to open access PDF if available",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "paper_id": {
+            "description": "Semantic Scholar paper ID",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "publication_date": {
+            "description": "Full publication date",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "publication_types": {
+            "description": "Publication type tags",
+            "items": {
+              "type": "string"
+            },
+            "type": "array"
+          },
+          "reference_count": {
+            "description": "Number of references in the paper",
+            "type": [
+              "number",
+              "null"
+            ]
+          },
+          "title": {
+            "description": "Paper title",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "tldr": {
+            "description": "TL;DR summary of the paper",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "url": {
+            "description": "URL to the paper",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "venue": {
+            "description": "Publication venue",
+            "type": [
+              "string",
+              "null"
+            ]
+          },
+          "year": {
+            "description": "Publication year",
+            "type": [
+              "number",
+              "null"
+            ]
+          }
+        },
+        "type": "object"
+      },
+      "type": "array"
+    },
+    "query": {
+      "description": "The search query used",
+      "type": "string"
+    },
+    "returned": {
+      "description": "Number of papers returned in this response",
+      "type": "number"
+    },
+    "total": {
+      "description": "Total number of matching papers",
+      "type": "number"
+    }
+  },
+  "required": [
+    "query",
+    "total",
+    "returned",
+    "papers"
+  ],
+  "type": "object"
+}
  2. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already provide safety hints; description adds value by specifying 'keyless' access and the source (Semantic Scholar) and scale (200M+ papers). 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?

Three concise sentences, no redundancy. Front-loaded with the main action, then specific use case, then options and keyless info.

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?

With an output schema present, the description doesn't need to detail returns. It covers purpose, usage, parameters, and uniqueness (keyless). Complete for a search 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 coverage is 100%, so the schema already documents all parameters. The description does not add significant new meaning beyond what the schema provides.

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 searches 200M+ academic papers on Semantic Scholar by keyword or exact title, and lists returned fields. It distinguishes itself from siblings like 'get_paper' and 'get_paper_citations'.

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

Usage Guidelines5/5

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

Explicitly states when to prefer this tool, e.g., citation count queries, and hints at filtering options. Provides clear context without mentioning alternatives but the use case guidance is strong.

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

All Semanticscholar 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

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, especially the core ones like get_paper, search_papers, and entity_profile. However, some pairs like ask_pipeworx and ask_pipeworx_grounded, or the polymarket tools, could cause momentary confusion, though descriptions help differentiate.

Naming Consistency2/5

Naming patterns are inconsistent: tools use verb_noun (e.g., get_paper), noun_phrase (e.g., polymarket_arbitrage), and bare verbs (e.g., forget, recall). There is no unifying pattern, and styles like 'pipeworx_feedback' vs 'search_papers' further add to the inconsistency.

Tool Count3/5

With 34 tools, the set covers a broad range of domains (academic papers, company data, prediction markets, memory, subscriptions). While the scope justifies the number, it feels slightly heavy and could benefit from consolidation or clearer grouping.

Completeness4/5

The tool set covers major functionalities for research, data retrieval, and monitoring, with only minor gaps (e.g., limited to US public companies, npm-only dependency scanning). Overall, the surface is comprehensive for the stated capabilities.

Resources