Scholarx

CLI or API | MCP

Version: 2.2.0

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

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.

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: Complete 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.

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. The operator 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.

MCP

This server use 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)

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

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/usage.md.

Dynamic Tool Selection & Visibility

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

The operator 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 the operator's 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.

MCP Configuration Examples

Install the connector-focused [mcp] extra. Examples use scholarx[mcp] to add FastMCP / FastAPI through agent-connector-sdk; the required Agent Connector SDK core still carries epistemic-graph[full]. The [agent-runtime] extra additionally enables model orchestration.

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

{
  "mcpServers": {
    "scholarx-mcp": {
      "command": "uvx",
      "args": [
        "--from",
        "scholarx[mcp]",
        "scholarx-mcp"
      ],
      "env": {
        "MCP_TOOL_MODE": "intent",
        "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"
      }
    }
  }
}

Runtime references require an alias-aware launcher such as Arbora. Other launchers must omit those entries and inject the resolved values through their own runtime secret boundary.

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": "127.0.0.1",
        "PORT": "8000",
        "MCP_TOOL_MODE": "intent",
        "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"
    }
  }
}

Run a reviewed container image as a least-privilege stdio child (no listener or published port):

docker run -i --rm \
  --read-only \
  --cap-drop=ALL \
  --security-opt=no-new-privileges \
  --pids-limit=256 \
  --tmpfs /tmp:rw,noexec,nosuid,nodev,size=64m \
  -e TRANSPORT=stdio \
  -e MCP_TOOL_MODE=intent \
  -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 \
  registry.example.invalid/scholarx@sha256:<digest> scholarx-mcp

For containerized network HTTP, supply an authenticated TLS ingress (or direct server TLS), exact MCP_ALLOWED_HOSTS, and an exact trusted-proxy CIDR policy through the operator-owned deployment profile. The generator does not emit an unauthenticated non-loopback listener.

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

Additional Deployment Options

scholarx can run as a local stdio process or container, or behind a remote network boundary. The Deployment guide carries the detailed transport contract.

• Local container — launch a reviewed immutable image as a least-privilege stdio child with no listener or published port.
• Remote URL — connect through an operator-supplied authenticated HTTPS ingress. Keep its URL, outbound identity references, trust profile, and exact MCP_ALLOWED_HOSTS in AgentConfig.

Agent

This repository features a fully integrated Pydantic AI Graph Agent. It communicates over the Agent Control Protocol (ACP) and interacts smoothly 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

version: '3.8'

services:
  scholarx-mcp:
    image: example/scholarx@sha256:<digest>
    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"

    image: example/scholarx@sha256:<digest>
    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/deployment.md.

Security & Governance

Built directly upon the enterprise-ready agent-connector-sdk 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

Environment Variables

Package environment variables

Inherited agent-connector-sdk variables (apply to every connector)

21 package + 14 inherited variable(s). Auto-generated from .env.example + the shared agent-connector-sdk set — do not edit.

The application can be configured using the following environment variables:

Installation

Pick the extra that matches what the operator want to run:

# Connector-focused MCP server (includes the shared graph engine)
uv pip install "scholarx[mcp]"

# Agent runtime (adds model orchestration to the shared graph engine)

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

Container images (:mcp)

One multi-stage docker/Dockerfile builds the MCP-only image (--target mcp):

docker build --target mcp   -t example/scholarx:mcp    docker/   # connector-focused MCP server

agent (immutable agent digest) with a co-located :mcp sidecar.

Knowledge-graph database (epistemic-graph)

Agent Connector SDK core dependency (epistemic-graph[full]). The [mcp] extra keeps deployments can use the bundled engine. For production or shared state, run epistemic-graph as a dedicated database service and configure the runtime to use it. Deployment recipes (single-node + Raft HA), connection configuration, and architecture diagrams are documented in the epistemic-graph deployment guide.

Documentation

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

AGENTS.md is the canonical contributor/agent guidance.

Repository Owners

Contribute

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

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

Governed capability contract

This package ships a compact canonical skill surface with specialist procedures kept as referenced workflows. The current MCP tools, skill metadata, connector_manifest.yml, ontology, mappings, shapes, fixtures, migrations, tool-schema fingerprints, and certification metadata form one versioned capability contract. Validate them together; do not rely on stale tool names or historical per-task skill wrappers.

Runtime endpoints, credentials, certificate trust, tenant identity, retention, and observability policy are deployment inputs and are never packaged values. See Configuration, trust, and privacy before enabling a network transport, connector ingestion, Arbora delegation, or trace export.