scholarx 1.0.1

Universal Research Paper API — single entry point for arXiv, PMC, bioRxiv, medRxiv, PsyArXiv, OSF, and Semantic Scholar

Scholarx

CLI or API | MCP | Agent

Version: 1.0.1

Documentation — Installation, deployment, usage across the API, CLI, MCP, and agent interfaces are maintained in the official documentation.

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Overview

Scholarx is a production-grade Agent and Model Context Protocol (MCP) server designed to interface directly with Universal Research Paper API — single entry point for arXiv, PMC, bioRxiv, medRxiv, PsyArXiv, OSF, and Semantic Scholar.

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Key Features

• Consolidated Action-Routed MCP Tools: Minimizes token overhead and eliminates tool bloat in LLM contexts by grouping methods into optimized, togglable tool modules.
• Enterprise-Grade Security: Comprehensive support for Eunomia policies, OIDC token delegation, and granular execution context tracking.
• Integrated Graph Agent: Built-in Pydantic AI agent supporting the Agent Control Protocol (ACP) and standard Web interfaces (AG-UI).
• Native Telemetry & Tracing: Out-of-the-box OpenTelemetry exports and native Langfuse tracing.

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CLI or API

This agent wraps the Universal Research Paper API — single entry point for arXiv, PMC, bioRxiv, medRxiv, PsyArXiv, OSF, and Semantic Scholar API. You can interact with it programmatically or via its integrated execution entrypoints.

Detailed instructions on how to use the underlying API wrappers, extended schema bindings, and developer SDK references are maintained in docs/index.md.

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MCP

This server utilizes dynamic Action-Routed tools to optimize token overhead and maximize IDE compatibility.

Available MCP Tools

The table below is auto-generated from the MCP server — do not edit by hand.

Condensed action-routed tools (default — MCP_TOOL_MODE=condensed)

MCP Tool	Toggle Env Var	Description
sx_info	DISCOVERYTOOL	Get metadata about sources and categories.
sx_search	SEARCHTOOL	Search for research papers across all configured sources.
sx_storage	STORAGETOOL	Manage offline PDF storage and background downloads.

Verbose 1:1 API-mapped tools (MCP_TOOL_MODE=verbose or both)

11 per-operation tools — one per public API method (click to expand)

MCP Tool	Toggle Env Var	Description
scholarx_download_paper	SCHOLAR_X_CLIENTTOOL	Download a paper's full PDF synchronously.
scholarx_download_papers	SCHOLAR_X_CLIENTTOOL	Download many papers in parallel with bounded concurrency.
scholarx_download_urls	SCHOLAR_X_CLIENTTOOL	Download arXiv PDFs directly by id/URL with bounded concurrency.
scholarx_get_download_status	SCHOLAR_X_CLIENTTOOL	Get the status of a queued download job.
scholarx_get_paper	SCHOLAR_X_CLIENTTOOL	Retrieve a single paper from a specific source.
scholarx_get_queue_status	SCHOLAR_X_CLIENTTOOL	Get the status of all queued downloads.
scholarx_get_recent_papers	SCHOLAR_X_CLIENTTOOL	Retrieve recently published papers.
scholarx_get_source_status	SCHOLAR_X_CLIENTTOOL	Get the status of all configured sources.
scholarx_list_categories	SCHOLAR_X_CLIENTTOOL	List available categories for each source.
scholarx_queue_download	SCHOLAR_X_CLIENTTOOL	Queue a paper for background downloading.
scholarx_search	SCHOLAR_X_CLIENTTOOL	Search across all configured sources with deduplication.

3 action-routed tool(s) (default) · 11 verbose 1:1 tool(s). Each is enabled unless its <DOMAIN>TOOL toggle is set false; MCP_TOOL_MODE selects the surface (condensed default · verbose 1:1 · both). Auto-generated — do not edit.

Detailed tool schemas, parameter shapes, and validation constraints are preserved in docs/mcp.md.

Dynamic Tool Selection & Visibility

This MCP server supports dynamic toolset selection and visibility filtering at runtime. This allows you to restrict the set of exposed tools in order to prevent blowing up the LLM's context window.

You can configure tool filtering via multiple input channels:

• CLI Arguments: Pass --tools or --toolsets (or their disabled counterparts --disabled-tools and --disabled-toolsets) during startup.
• Environment Variables: Define standard environment variables:
  • MCP_ENABLED_TOOLS / MCP_DISABLED_TOOLS
  • MCP_ENABLED_TAGS / MCP_DISABLED_TAGS
• HTTP SSE Request Headers: Pass custom headers during transport initialization:
  • x-mcp-enabled-tools / x-mcp-disabled-tools
  • x-mcp-enabled-tags / x-mcp-disabled-tags
• HTTP SSE Request Query Parameters: Append query parameters directly to your transport connection URL:
  • ?tools=tool1,tool2
  • ?tags=tag1

When query strings or parameters are supplied, an LLM-free Knowledge Graph resolution layer (using DynamicToolOrchestrator) matches query intents against known tool tags, names, or descriptions, with safe fallback and automated 24-hour background cache refreshing.

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MCP Configuration Examples

Install the slim [mcp] extra. All examples install scholarx[mcp] — the MCP-server extra that pulls only the FastMCP / FastAPI tooling (agent-utilities[mcp]). It deliberately excludes the heavy agent runtime (pydantic-ai, the epistemic-graph engine, dspy, llama-index), so uvx / container installs are far smaller. Use the full [agent] extra only when you need the integrated Pydantic AI agent.

stdio Transport (local IDEs — Cursor, Claude Desktop, VS Code)

{
  "mcpServers": {
    "scholarx-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "scholarx[mcp]",
        "scholarx-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "condensed",
        "DISCOVERYTOOL": "True",
        "NCBI_API_KEY": "your_ncbi_api_key_here",
        "OSF_TOKEN": "your_osf_token_here",
        "S2_API_KEY": "your_s2_api_key_here",
        "SEARCHTOOL": "True",
        "STORAGETOOL": "True"
      }
    }
  }
}

Streamable-HTTP Transport (networked / production)

{
  "mcpServers": {
    "scholarx-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "scholarx[mcp]",
        "scholarx-mcp",
        "--transport",
        "streamable-http",
        "--port",
        "8000"
      ],
      "env": {
        "TRANSPORT": "streamable-http",
        "HOST": "0.0.0.0",
        "PORT": "8000",
        "MCP_TOOL_MODE": "condensed",
        "DISCOVERYTOOL": "True",
        "NCBI_API_KEY": "your_ncbi_api_key_here",
        "OSF_TOKEN": "your_osf_token_here",
        "S2_API_KEY": "your_s2_api_key_here",
        "SEARCHTOOL": "True",
        "STORAGETOOL": "True"
      }
    }
  }
}

Alternatively, connect to a pre-deployed Streamable-HTTP instance by url:

{
  "mcpServers": {
    "scholarx-mcp": {
      "url": "http://localhost:8000/scholarx-mcp/mcp"
    }
  }
}

Deploying the Streamable-HTTP server via Docker:

docker run -d \
  --name scholarx-mcp-mcp \
  -p 8000:8000 \
  -e TRANSPORT=streamable-http \
  -e HOST=0.0.0.0 \
  -e PORT=8000 \
  -e MCP_TOOL_MODE=condensed \
  -e DISCOVERYTOOL=True \
  -e NCBI_API_KEY=your_ncbi_api_key_here \
  -e OSF_TOKEN=your_osf_token_here \
  -e S2_API_KEY=your_s2_api_key_here \
  -e SEARCHTOOL=True \
  -e STORAGETOOL=True \
  knucklessg1/scholarx:mcp

Auto-generated from the code-read env surface (MCP_TOOL_MODE + package vars) — do not edit.

Additional Deployment Options

scholarx can also run as a local container (Docker / Podman / uv) or be consumed from a remote deployment. The Deployment guide has full, copy-paste mcp_config.json for all four transports — stdio, streamable-http, local container / uv, and remote URL:

• Local container / uv — launch the server from mcp_config.json via uvx, docker run, or podman run, or point at a local streamable-http container by url.
• Remote URL — connect to a server deployed behind Caddy at http://scholarx-mcp.arpa/mcp using the "url" key.

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts seamlessly with the Agent Web UI (AG-UI) and Terminal interface.

Running the Agent CLI

To start the interactive command-line agent:

# Set credentials
export SCHOLARX_STORAGE_DIR="your_value"
export DEBUG="your_value"
export PYTHONUNBUFFERED="your_value"
export SERVICENOW_INSTANCE="your_value"
export SERVICENOW_USERNAME="your_value"
export OSF_TOKEN="your_value"
export S2_API_KEY="your_value"
export NCBI_API_KEY="your_value"
export SERVICENOW_PASSWORD="your_value"

# Run the agent server
scholarx-agent --provider openai --model-id gpt-4o

Docker Compose Orchestration

The following docker/agent.compose.yml configures the Agent, Web UI, and Terminal Interface together:

version: '3.8'

services:
  scholarx-mcp:
    image: knucklessg1/scholarx:latest
    container_name: scholarx-mcp
    hostname: scholarx-mcp
    restart: always
    env_file:
      - ../.env
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=8004
      - TRANSPORT=streamable-http
    ports:
      - "8004:8004"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8004/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

  scholarx-agent:
    image: knucklessg1/scholarx:latest
    container_name: scholarx-agent
    hostname: scholarx-agent
    restart: always
    depends_on:
      - scholarx-mcp
    env_file:
      - ../.env
    command: [ "scholarx-agent" ]
    environment:
      - PYTHONUNBUFFERED=1
      - HOST=0.0.0.0
      - PORT=9600
      - MCP_URL=http://scholarx-mcp:8004/mcp
      - PROVIDER=${PROVIDER:-openai}
      - MODEL_ID=${MODEL_ID:-gpt-4o}
      - ENABLE_WEB_UI=True
      - ENABLE_OTEL=True
    ports:
      - "9600:9600"
    healthcheck:
      test: ["CMD", "python3", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:9600/health')"]
      interval: 30s
      timeout: 10s
      retries: 3
      start_period: 10s
    logging:
      driver: json-file
      options:
        max-size: "10m"
        max-file: "3"

Detailed graph node architecture explanations, custom skill configurations, and agentic trace guides are available in docs/agent.md.

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Security & Governance

Built directly upon the enterprise-ready agent-utilities core, standard security parameters are fully supported:

Access Control & Policy Enforcement

• Eunomia Policies: Fine-grained, policy-driven tool authorization. Supports none, local embedded (mcp_policies.json), or centralized remote modes.
• OIDC Token Delegation: Compliant with RFC 8693 token exchange for flowing authenticating user credentials from Web UI / ACP → Agent → MCP.
• Scoped Credentials: Execution context runs restricted to the specific caller identity.

Runtime Security Grid

Feature	Functionality	Enablement
Tool Guard	Sensitivity inspection with human-in-the-loop validation	Enabled by default
Prompt Injection Defense	Input scanning, repetition monitoring, and recursive loop blocks	Enabled by default
Context Safety Guard	Stuck-loop detectors and contextual overflow preemptive alerts	Enabled by default

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Environment Variables

Package environment variables

Variable	Example	Description
HOST	0.0.0.0
PORT	8004
TRANSPORT	stdio	options: stdio, streamable-http, sse
AUTH_TYPE	none	options: none, basic, custom
DEFAULT_AGENT_NAME	ScholarX Agent
ENABLE_OTEL	True
OTEL_EXPORTER_OTLP_ENDPOINT	http://localhost:8080/api/public/otel
OTEL_EXPORTER_OTLP_PUBLIC_KEY	pk-...
OTEL_EXPORTER_OTLP_SECRET_KEY	sk-...
OTEL_EXPORTER_OTLP_PROTOCOL	http/protobuf
EUNOMIA_TYPE	none	options: none, embedded, remote
EUNOMIA_POLICY_FILE	mcp_policies.json
EUNOMIA_REMOTE_URL	http://eunomia-server:8000
DEBUG	False
PYTHONUNBUFFERED	1
OSF_TOKEN	your_osf_token_here	OSF / PsyArXiv
S2_API_KEY	your_s2_api_key_here	Semantic Scholar
NCBI_API_KEY	your_ncbi_api_key_here	PubMed Central (NCBI E-utilities)
SEARCHTOOL	True
DISCOVERYTOOL	True
STORAGETOOL	True

Inherited agent-utilities variables (apply to every connector)

Variable	Example	Description
MCP_TOOL_MODE	condensed	Tool surface: condensed
MCP_ENABLED_TOOLS	—	Comma-separated tool allow-list
MCP_DISABLED_TOOLS	—	Comma-separated tool deny-list
MCP_ENABLED_TAGS	—	Comma-separated tag allow-list
MCP_DISABLED_TAGS	—	Comma-separated tag deny-list
MCP_CLIENT_AUTH	—	Outbound MCP auth (oidc-client-credentials for fleet calls)
OIDC_CLIENT_ID	—	OIDC client id (service-account auth)
OIDC_CLIENT_SECRET	—	OIDC client secret (service-account auth)
MCP_URL	http://localhost:8000/mcp	URL of the MCP server the agent connects to
PROVIDER	openai	LLM provider for the agent
MODEL_ID	gpt-4o	Model id for the agent
ENABLE_WEB_UI	True	Serve the AG-UI web interface

21 package + 12 inherited variable(s). Auto-generated from .env.example + the shared agent-utilities set — do not edit.

The application can be configured using the following environment variables:

Variable	Type	Default	Description
HOST	String	0.0.0.0	Host IP address to bind the servers to.
PORT	Integer	8004	Port number to run the servers on.
TRANSPORT	String	stdio	MCP transport type (stdio, streamable-http, sse).
AUTH_TYPE	String	none	Authentication type for access control (none, basic, custom).
DEFAULT_AGENT_NAME	String	ScholarX Agent	Custom display name for the Pydantic AI Graph Agent.
ENABLE_OTEL	Boolean	True	Enable OpenTelemetry tracing and exports.
EUNOMIA_TYPE	String	none	Eunomia policy evaluation mode (none, embedded, remote).
EUNOMIA_POLICY_FILE	String	mcp_policies.json	Path to the local Eunomia policy configuration file.
EUNOMIA_REMOTE_URL	String		Centralized Eunomia server endpoint.
SCHOLARX_STORAGE_DIR	String	~/.local/share/scholarx/papers	Directory path where downloaded PDF papers are cached.
DEBUG	Boolean	False	Enable verbose debugging mode.
PYTHONUNBUFFERED	Integer	1	Forces stdout and stderr to be unbuffered.
SERVICENOW_INSTANCE	String		ServiceNow instance base URL.
SERVICENOW_USERNAME	String		ServiceNow account username.
SERVICENOW_PASSWORD	String		ServiceNow account password.
OSF_TOKEN	String		API Access Token for OSF integration.
S2_API_KEY	String		Semantic Scholar API Key to bypass public rate limits.
NCBI_API_KEY	String		NCBI API Key for PubMed Central (PMC) queries.
SEARCHTOOL	Boolean	True	Toggle to enable/disable Search MCP tool category.
DISCOVERYTOOL	Boolean	True	Toggle to enable/disable Discovery MCP tool category.
STORAGETOOL	Boolean	True	Toggle to enable/disable Storage MCP tool category.

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Installation

Pick the extra that matches what you want to run:

Extra	Installs	Use when
scholarx[mcp]	Slim MCP server only (agent-utilities[mcp] — FastMCP/FastAPI)	You only run the MCP server (smallest install / image)
scholarx[agent]	Full agent runtime (agent-utilities[agent,logfire] — Pydantic AI + the epistemic-graph engine)	You run the integrated agent
scholarx[all]	Everything (mcp + agent)	Development / both surfaces

# MCP server only (recommended for tool hosting — slim deps)
uv pip install "scholarx[mcp]"

# Full agent runtime (Pydantic AI + epistemic-graph engine)
uv pip install "scholarx[agent]"

# Everything (development)
uv pip install "scholarx[all]"      # or: python -m pip install "scholarx[all]"

Container images (:mcp vs :agent)

One multi-stage docker/Dockerfile builds two right-sized images, selected by --target:

Image tag	Build target	Contents	Entrypoint
knucklessg1/scholarx:mcp	--target mcp	scholarx[mcp] — slim, no engine/pydantic-ai/dspy/llama-index/tree-sitter	scholarx-mcp
knucklessg1/scholarx:latest	--target agent (default)	scholarx[agent] — full agent runtime + epistemic-graph engine	scholarx-agent

docker build --target mcp   -t knucklessg1/scholarx:mcp    docker/   # slim MCP server
docker build --target agent -t knucklessg1/scholarx:latest docker/   # full agent

docker/mcp.compose.yml runs the slim :mcp server; docker/agent.compose.yml runs the agent (:latest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

The full agent ([agent] / :latest) embeds the epistemic-graph engine (pulled in transitively via agent-utilities[agent]). For production — or to share one knowledge graph across multiple agents — run epistemic-graph as its own database container and point the agent at it instead of embedding it. Deployment recipes (single-node + Raft HA), connection config, and the full database architecture (with diagrams) are documented in the epistemic-graph deployment guide. The slim [mcp] server does not require the database.

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Documentation

The complete documentation is published as the official documentation site and is the recommended reference for installation, deployment, and day-to-day operation.

Page	Contents
Installation	pip, source, extras, prebuilt Docker image
Deployment	run the MCP server and the agent, Compose, Caddy + Technitium, env config
Usage	the MCP tools, the ScholarXClient API, the CLI
Overview	ecosystem role, enterprise readiness, architecture
Concepts	concept registry (CONCEPT:SX-*)
Coverage Report	per-source coverage and verification

AGENTS.md is the canonical contributor/agent guidance.

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Repository Owners

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Contribute

Contributions are welcome! Please ensure code quality by executing local checks before submitting pull requests:

• Format code using ruff format .
• Lint code using ruff check .
• Validate type-safety with mypy .
• Execute test suites using pytest

Deploy with agent-os-genesis

This package can be provisioned for you — skill-guided — by the agent-os-genesis universal skill (its single-package deploy mode): it picks your install method, seeds secrets to OpenBao/Vault (or .env), trusts your enterprise CA, registers the MCP server, and verifies it — the same machinery that stands up the whole Agent OS, narrowed to just this package. Ask your agent to "deploy scholarx with agent-os-genesis".

Install mode	Command
Bare-metal, prod (PyPI)	uvx scholarx-mcp · or uv tool install scholarx
Bare-metal, dev (editable)	uv pip install -e ".[all]" · or pip install -e ".[all]"
Container, prod	deploy knucklessg1/scholarx:latest via docker-compose / swarm / podman / podman-compose / kubernetes
Container, dev (editable)	deploy docker/compose.dev.yml (source-mounted at /src; edits live on restart)

Secrets are read-existing + seeded via vault_sync — you are only prompted for what's missing.