In-depth architectural comparison of the Octagon Deep Research MCP and Pfc MCP MCP servers. Compare execution transports, security boundaries, tool capabilities, quality scores, and ready-to-paste client installation snippets for Claude, Cursor, Windsurf, and VS Code.
At a Glance & Executive Verdict
Octagon Deep Research MCP
Research · Local stdio
Quality: 44/100 (Fair) | Auth: API Key required
Pfc MCP
Research · Local stdio
Quality: 64/100 (Good) | Auth: No auth required
Verdict Summary: Choose Octagon Deep Research MCP if you need specialized Research tools running via a local process. Choose Pfc MCP if your workspace requires Research integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Octagon Deep Research MCP when:
You need dedicated capabilities in the Research domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: API Key required (Freemium).
You have access to required keys: OCTAGON_API_KEY.
Primary tools included: Unlimited parallel deep research queries with no rate limits, Aggregates and cross-verifies data from multiple sources, Supports broad domain coverage including technology, healthcare, environment, and policy.
MCP server for ITASCA PFC discrete element simulation — browse documentation, execute scripts, capture plots, and manage long-running tasks via a WebSocket bridge to the PFC GUI.
Unlimited parallel deep research queries with no rate limits
Aggregates and cross-verifies data from multiple sources
Supports broad domain coverage including technology, healthcare, environment, and policy
Generates detailed research reports and comparative analyses
Integrates universally with MCP clients via command invocation
Pfc MCP Tools (10)
itasca_browse_commands
Browse 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")
Ready-to-Paste Client Configurations
Paste either (or both) of these JSON server blocks into your client config file (e.g. claude_desktop_config.json or ~/.cursor/mcp.json).
Octagon Deep Research MCP is categorized under Research and uses a local stdio subprocess. In contrast, Pfc MCP belongs to Research using local stdio subprocess. Select Octagon Deep Research MCP when you need capabilities focused on research and Pfc MCP when you require tools for research.
Browse 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 keywords
itasca_query_command
Search 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 keywords
itasca_query_python_api
Search 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 keywords
itasca_execute_task
Submit 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_status
Check 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_tasks
List tracked Itasca tasks with pagination.
itasca_interrupt_task
Request graceful interruption of a running Itasca task.
itasca_execute_code
Execute 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.