Public MCP endpoint
https://scorecompute.com/mcp
Choose a client that supports remote MCP servers over Streamable HTTP. This public alpha requires no API key. Configuration field names vary by client; a URL-based example is:
{
"mcpServers": {
"scorecompute": {
"url": "https://scorecompute.com/mcp"
}
}
}Protocol sequence
- Initialize the MCP connection with the software client name and version.
- Use
tools/listto discover fifteen tools and their current input schemas: twelve direct computation tools and three optional mission tools. - Call a tool with
tools/call, preserving units and parameter bounds. - Read the structured result, assumptions and provenance. Close the session when finished.
This is a stateful endpoint. Clients retain the Mcp-Session-Id returned by initialization. Sessions expire after inactivity; reinitialize after a session-expired response.
Direct tools stay independent
Call any of the twelve computation tools directly. For example, ask optimize_tasks to select a high-value schedule, then pass its selected durations to simulate_plan if you want to choose each step yourself. No orchestrator is required to use an engine.
Optional native mission composition
list_capabilities describes contracts, units, assumptions, execution backends and composition readiness, with an optional exact theme filter. Only the scheduling pair is composable in this version. plan_mission validates a request and previews its candidate budgets and work limits without running engines.
build_robust_plan compares up to three task selections using nominal, 10% and 20% time reserves. Native Rust validates the integer-minute handoff, uses exact worst-case bounds when possible, and otherwise runs fixed seeded Monte Carlo. Identical selections reuse their evaluation within that mission. This is one composite MCP call: its internal engines execute natively, without fabricated nested MCP requests.
{
"name": "build_robust_plan",
"arguments": {
"items": [
{
"name": "A",
"duration": 60,
"value": 90
},
{
"name": "B",
"duration": 40,
"value": 70
},
{
"name": "C",
"duration": 20,
"value": 20
}
],
"budget": 100,
"uncertainty": 0.25,
"min_success_rate": 0.95,
"samples": 10000,
"seed": 42,
"max_candidates": 3,
"duration_model": "independent_uniform",
"allowed_tools": [
"optimize_tasks",
"simulate_plan"
]
}
}Required inputs are tasks, budget and uncertainty. The other fields above show the defaults. Budgets and durations use integer minutes. Missions accept up to 32 tasks, a 1–1440 minute budget, uncertainty from 0 to 0.5, 100–20000 samples per candidate and at most three candidates. Unsupported duration models and missing tool permissions return explicit blockers; no external commands or destinations are accepted.
The selected candidate has the highest value among examined candidates whose simultaneous 95% Hoeffding lower bound meets the success target under the declared independent uniform duration model. If no candidate qualifies, the result is inconclusive with selected: null. An exact bound can replace simulation; in that case simulation: null is intentional. These bounds describe the supplied model, not real-world delivery guarantees or a globally optimal robust schedule.
Actual native events stream through MCP progress messages prefixed SC_MISSION_EVENT:. The web composer displays these events, measured engine calls, simulation work and the final decision. Mission inputs, candidate details and event payloads remain private to the request.
Limits and errors
Request bodies are capped at 1 MiB. One Rust calculation runs at a time; busy requests may return a tool error or HTTP 429. The public transport limits concurrent connections and sessions. A session can expire, the worker can be offline, and expensive calls can time out. Retry with backoff when appropriate; there is no persistent job queue or automatic failover.
Identity and the public observatory
Software names and versions are self-reported. ScoreCompute cannot infer or verify the underlying AI model from clientInfo. Recent tool activity and the declared client name appear on the public website. Do not put secrets or personal data in this identity. Calculation arguments and results are not published in the observatory.
Interpreting evidence
Physical consistency is conditional on the model and inputs. It does not prove image authenticity. Statistical flags do not establish misconduct, cheating or market manipulation. Keep the returned assumptions and limitations with every explanation.