Scholar vs Pfc MCP
A side-by-side look at the Scholar and Pfc MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Scholar for research work as a local process; choose Pfc MCP for research. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Scholar when
- You need dedicated capabilities in the Research domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
SCOPUS_API_KEY, S2_API_KEY, SCIHUB_MIRRORS, LIBGEN_MIRRORS, SCOPUS_INST_TOKEN, CONTACT_EMAIL. - You want tools like
search_papers_tool, search_papers_s2_tool, get_paper_details_tool.
Choose Pfc MCP when
- You need dedicated capabilities in the Research domain.
- Its auth model fits you: No auth required.
- You want tools like
itasca_browse_commands, itasca_browse_python_api, itasca_browse_reference.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Research | Research |
| Quality signal | 48/100 · Fair | 61/100 · Good |
| Install command | Confirmed | Not confirmed yet |
| Transport | Local subprocess (stdio) | — |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | SCOPUS_API_KEYS2_API_KEYSCIHUB_MIRRORSLIBGEN_MIRRORSSCOPUS_INST_TOKENCONTACT_EMAIL | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 0 | 220 |
| Directory activity | 1 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 27 | 10 |
| Full listing | View Scholar | View Pfc MCP |
Tools & capabilities
Scholar tools 27
search_papers_toolSearch papers via Scopus (Boolean syntax) or OpenAlex. Sort by `cited_by_count` or `publication_year`.search_papers_s2_toolSearch papers via Semantic Scholar. Note: strictly rate-limited to 1 request/sec.get_paper_details_toolFetch full metadata and abstract by Scopus ID, DOI, or OpenAlex ID (with automatic routing).get_paper_details_s2_toolFetch full metadata from Semantic Scholar, including **AI-generated TLDRs**. Accepts S2 ID or DOI.search_titles_unpaywall_toolSearch Unpaywall's database directly by title. Set `is_oa=True` for strictly OA results.get_related_works_toolFind related/similar papers using OpenAlex's bibliographic coupling.search_books_toolSearch for book metadata via Google Books or Open Library.get_book_details_toolFetch complete book details, descriptions, and ISBNs.search_libgen_toolSearch Library Genesis for books to retrieve their download MD5 hashes.interact_with_book_toolSmart extraction from Libgen. Actions: `toc` (Table of Contents), `search` (keywords), `pages` (range).autocomplete_authors_toolRapidly disambiguate author names and resolve OpenAlex Author IDs.search_authors_toolDetailed bibliometric profiles: H-index, i10-index, ORCID, and research concepts.- +15 more on the listing page
Pfc MCP tools 10
itasca_browse_commandsBrowse Itasca command documentation by path (like glob + cat). Navigation levels: - No command: All command categories overview - Category only (e.g., "ball"): List commands in category - Full command (e.g., "ball create"): Full documentation When to use: - You know the command category or exact command - You want to explore available commands Related tools: - itasca_query_command: Search commands by keywords (when path unknown) - itasca_browse_reference: Browse reference docs (e.g., "contact-models linear")itasca_browse_python_apiBrowse Itasca Python SDK documentation by path (like glob + cat).itasca_browse_referenceBrowse ITASCA reference documentation (syntax elements, model properties). Works across engines via the required ``software`` selector (pfc/flac/3dec). References are language elements used within commands, not standalone commands. Navigation levels: - No topic: All reference categories for the engine - Category (e.g., "constitutive-models"): List items in category - Full path (e.g., "constitutive-models mohr-coulomb"): Full documentation - Sub-item path (e.g., "plot-items zone contour"): Sub-item details When to use: - Need material/contact/joint model property names (kn, ks, fric, cohesion, friction, ...) - Need range filtering syntax (position, cylinder, group, id) - Need plot item configuration (contour, label, color-by, cut, transparency, legend) - Setting up model-assignment commands (e.g. "... cmodel assign ... property ...") - Using range filters in any command - Configuring "plot item create" commands Related tools: - itasca_browse_commands: Command syntax (e.g., "zone create") - itasca_query_command: Search commands by keywordsitasca_query_commandSearch Itasca command documentation by keywords (like grep). Returns matching command paths. Use itasca_browse_commands for full documentation. When to use: - You have keywords but don't know exact command path - Example: "ball create", "contact property", "model solve" Related tools: - itasca_browse_commands: Get full documentation for a known command path - itasca_browse_reference: Browse reference docs (e.g., "contact-models linear") - itasca_query_python_api: Search Python SDK by keywordsitasca_query_python_apiSearch Itasca Python SDK documentation by keywords (like grep). Returns matching API paths with signatures. Use itasca_browse_python_api for full documentation. When to use: - You have keywords but don't know exact API path - Example: "ball velocity", "create", "contact force" Related tools: - itasca_browse_python_api: Get full documentation for a known API path - itasca_query_command: Search Itasca commands by keywordsitasca_execute_taskSubmit a Python script file for asynchronous execution in the Itasca engine. Returns a task_id immediately; the script runs in the background. Companion tools manage the lifecycle: - itasca_check_task_status: poll output, progress, and final status - itasca_interrupt_task: cancel a running task - itasca_list_tasks: browse task history For synchronous, inline execution, use itasca_execute_code instead. While the task is cycling, itasca_execute_code shares the same __main__ namespace in the engine's main thread, so you can inspect or modify simulation state live — probe progress, tune parameters mid-run, swap callbacks, or trigger early termination via a sentinel variable. Submit with reasonable starting values and refine as the task runs. Console output from itasca.command() inside the script (table dumps, list output, command summaries) is captured into the task log alongside Python prints, visible through itasca_check_task_status. Script-writing rules: - Multi-line itasca.command("""...""") batches are normalized to one engine call per command, keeping the task interruptible mid-batch. Normalization recognizes itasca.command through its import name; call it through that name directly — rebinding via intermediate variables (`_it = itasca`) bypasses it and logs a bridge warning. - Pass each FISH definition block (`fish define` / `fish operator` / legacy bare `define`, through its terminating standalone `end`) whole, in ONE itasca.command() string; feeding one line-by-line leaves the engine blocked in interactive FISH mode until completed manually in the GUI console. Per-line loops are fine for ordinary commands. - `program call '<file>'` (.p3dat / .p2dat / .dat / ...) is supported: the bridge runs the file inline, one command per engine call, so the bridge stays reachable, the task stays interruptible, and the file's output lands in the task log command by command.itasca_check_task_statusCheck status and paginated output for a submitted Itasca task. Output combines Python prints and Itasca console output from itasca.command() calls (table dumps, list output, command summaries) interleaved in execution order. Use skip_newest / limit to paginate, or filter to keep only matching lines.itasca_list_tasksList tracked Itasca tasks with pagination.itasca_interrupt_taskRequest graceful interruption of a running Itasca task.itasca_execute_codeExecute Python code synchronously in the running Itasca engine process. Returns stdout and an optional result variable immediately. Code runs in the engine's main thread, sharing the same __main__ namespace as any running task — side effects persist and are immediately visible to the task on its next cycle. The tool stays responsive while a task submitted via itasca_execute_task is cycling (calls interleave at cycle gaps), so use it as a live REPL — nothing has to be pre-scripted into the task up front. Typical uses: - Query model state and read Itasca command output: itasca.command('ball list') etc. — table dumps, list output, and command summaries are captured and interleaved with Python prints in execution order - Live inspection and tuning during a running task: check forces or contact statistics, modify parameters, swap callbacks, set sentinel variables the task reads each cycle - Create and export plots: itasca.command('plot ...') Environment: the engine's embedded Python interpreter (Itasca 9+ → Python 3.10, pre-9 → Python 3.6 or older; the product+version is encoded in sys.executable, e.g. PFC900). When unsure, check sys.version_info before relying on newer syntax. Code-writing rules (shared with itasca_execute_task): - Multi-line itasca.command("""...""") batches are normalized to one engine call per command, keeping the bridge reachable mid-batch. Normalization recognizes itasca.command through its import name; call it through that name directly — rebinding via intermediate variables (`_it = itasca`) bypasses it and the output carries a bridge warning. - Pass each FISH definition block (`fish define` / `fish operator` / legacy bare `define`, through its terminating standalone `end`) whole, in ONE itasca.command() string; feeding one line-by-line leaves the engine blocked in interactive FISH mode until completed manually in the GUI console. Per-line loops are fine for ordinary commands. - `program call '<file>'` (.p3dat / .p2dat / .dat / ...) is supported: the bridge runs the file inline, one command per engine call, so the bridge stays reachable, the run stays interruptible, and the file's output arrives command by command. Synchronous semantics: the request blocks until the code finishes or hits the timeout (default 10s, max 600s), and output is returned in full. The call is not tracked by itasca_list_tasks and cannot be interrupted mid-execution. For cancellable, pollable, or background work, submit via itasca_execute_task instead.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Scholar
{
"mcpServers": {
"scholar": {
"command": "uvx",
"args": [
"scholar-academic-mcp"
],
"env": {
"SCOPUS_API_KEY": "YOUR_SCOPUS_API_KEY_HERE",
"S2_API_KEY": "YOUR_S2_API_KEY_HERE",
"SCIHUB_MIRRORS": "YOUR_SCIHUB_MIRRORS_HERE",
"LIBGEN_MIRRORS": "YOUR_LIBGEN_MIRRORS_HERE",
"SCOPUS_INST_TOKEN": "YOUR_SCOPUS_INST_TOKEN_HERE",
"CONTACT_EMAIL": "YOUR_CONTACT_EMAIL_HERE"
}
}
}
}
Pfc MCP
No confirmed install command for Pfc MCP yet — follow the setup steps linked from its listing page rather than a guessed package name.
Frequently asked questions
Scholar is categorized under Research and uses a local stdio subprocess. Pfc MCP belongs to Research and uses an install path we have not confirmed yet. Pick Scholar for research work and Pfc MCP for research.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client. Pfc MCP has no confirmed install command yet — follow its own setup instructions.
Scholar has no declared pricing — check the project's own site. Pfc MCP is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add scholar and yusong652-pfc-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Scholar has a quality signal of 48/100 (Fair) with 1 views, 0 installs and 0 GitHub stars. Pfc MCP has 61/100 (Good) with 3 views, 0 installs and 220 GitHub stars.