HasData Google Scholar vs Pfc MCP
A side-by-side look at the HasData Google 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 HasData Google Scholar for research work as a local process; choose Pfc MCP for research as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose HasData Google Scholar when
- You need dedicated capabilities in the Research domain.
- You prefer a local stdio subprocess transport.
- You have the keys it needs:
HASDATA_API_KEY. - You want tools like
hasdata_google_scholar_case_law_getScholarCaseLawOpinion, hasdata_google_scholar_cite_getScholarCitationFormats, hasdata_google_scholar_scholar_getScholarSearchResults.
Choose Pfc MCP when
- You need dedicated capabilities in the Research domain.
- You prefer a local stdio subprocess transport.
- 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 | 63/100 · Good | 64/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | Local subprocess (stdio) |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | HASDATA_API_KEY | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 11 | 219 |
| Directory activity | 1 views 0 installs 0 upvotes | 3 views 0 installs 0 upvotes |
| Tools | 3 | 10 |
| Full listing | View HasData Google Scholar | View Pfc MCP |
Tools & capabilities
HasData Google Scholar tools 3
hasdata_google_scholar_case_law_getScholarCaseLawOpinionGet Scholar Case Law Opinion Looks up a Google Scholar Case Law opinion by its `caseId` (as returned in a google/scholar organic result's `caseId`, when searching with `asSdt` set to a case-law court scope, or in another case-law result's `citedCases`). Returns the case title, party names, court, procedural history, argued/decided dates, short citations, docket numbers, and every case cited within the opinion (grouped by cited case with a ready-to-call `hasdataLink`). Use to build legal-research and case-law-analysis workflows, or to walk a citation graph one cited case at a time.hasdata_google_scholar_cite_getScholarCitationFormatsGet Scholar Citation Formats Looks up citation formats and export links for a single Google Scholar organic search result, identified by its `resultId` (as returned in a google/scholar organic result). Returns formatted citation snippets (MLA, APA, Chicago, Harvard, Vancouver) and reference-manager export links (BibTeX, EndNote, RefMan, RefWorks). Use to build citation/bibliography features or complete a research workflow started with google/scholar.hasdata_google_scholar_scholar_getScholarSearchResultsGet Scholar Search Results Scrapes Google Scholar for a query with author:/source: search helpers, year range (asYlo/asYhi), cited-by and all-versions lookups (cites/cluster), review-article and citation-inclusion filters, language/language-restrict, start/num pagination, and asSdt to scope to case law (asSdt=2006 for all courts, 2003 for Federal courts only, or 4/6/3 plus comma-separated court codes for specific courts) or toggle patents. Returns each organic result with title, link, snippet, publication info (authors with profile links), cited-by count and link, related-articles link, and all-versions count and link; a case-law result links to a scholar_case?case= page and carries a ready-to-call caseHasdataLink. Use for academic research, literature review automation, citation tracking, grounding research agents with scholarly sources, and walking case law straight into google/scholar-case-law.
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.
HasData Google Scholar
{
"mcpServers": {
"hasdata-google-scholar": {
"command": "npx",
"args": [
"-y",
"@hasdata/google-scholar-mcp"
],
"env": {
"HASDATA_API_KEY": "YOUR_HASDATA_API_KEY_HERE"
}
}
}
}
Pfc MCP
{
"mcpServers": {
"yusong652-pfc-mcp": {
"command": "uvx",
"args": [
"itasca-mcp"
]
}
}
}
Frequently asked questions
HasData Google Scholar is categorized under Research and uses a local stdio subprocess. Pfc MCP belongs to Research and uses a local stdio subprocess. Pick HasData Google 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.
HasData Google 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 hasdata-google-scholar and yusong652-pfc-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, HasData Google Scholar has a quality signal of 63/100 (Good) with 1 views, 0 installs and 11 GitHub stars. Pfc MCP has 64/100 (Good) with 3 views, 0 installs and 219 GitHub stars.