RealRun
Official# RealRun MCP server
<img src="assets/realrun-icon-400.png" alt="RealRun logo" width="96" height="96">
[](https://mcpservers.org/servers/baseledgerip/realrun-mcp)
Read-only lookups over [RealRun](https://realrun.baseledgerip.com)'s published, independent tests of AI models on everyday tasks (inbox triage, meeting scheduling, receipts, small-business finance, customer support replies). It lets an agent ask "which model should I use for task X?" and get scores and safety warnings back.
## Connect (remote, no install)
Streamable HTTP endpoint: `https://realrun.baseledgerip.com/mcp`
```json
{ "mcpServers": { "realrun": { "url": "https://realrun.baseledgerip.com/mcp" } } }
```
Claude Code: `claude mcp add --transport http realrun https://realrun.baseledgerip.com/mcp`
Cline: add this to `cline_mcp_settings.json` (keep `"type": "streamableHttp"`; without it Cline falls back to SSE and the connection fails). Full agent-readable steps: [llms-install.md](llms-install.md).
```json
{ "mcpServers": { "realrun": { "type": "streamableHttp", "url": "https://realrun.baseledgerip.com/mcp", "disabled": false, "autoApprove": [] } } }
```
## Run locally (stdio)
```
npx -y realrun-mcp
```
(once published to npm; from a checkout: `npm install && node src/stdio.mjs`)
## Tools
| Tool | Input | Returns |
|---|---|---|
| `list_tasks` | none | Tested tasks, item counts, dates, results pages, playbook prices |
| `get_scores` | `task` | Every model's headline score (best first), cost tier, metric definition |
| `recommend_model` | `task`, `priority` (`accuracy`/`cost`/`balanced`), `include_subscription` | One pick + 2 alternatives; never picks a model flagged not recommended |
| `get_warnings` | `task` | Not-recommended and caution flags (scam failures, invented data, inconsistency) |
Task ids: `inbox-triage`, `meeting-scheduling`, `receipts`, `finance`, `support-replies`.
All tools are read-only. The server stores nothing and has no auth. It is stateless, with JSON responses.
## Data
`src/data.json` holds only the published headline numbers. Full prompts and item-level results are in the paid playbooks ($29 each, links in `list_tasks`). Scores reflect our tests on the dates shown, not a guarantee.
## Billing (not active)
Every call passes through `src/metering.mjs`. The plan is a free allowance of about 20 lookups a day, then per-call billing via Stripe or x402. It is **off**: nobody is charged, and no payment objects exist.
## Test
`npm test` runs stdio end to end. `node test/smoke.mjs https://realrun.baseledgerip.com/mcp` tests the remote endpoint.
## Layout
- `src/server.mjs`: tools
- `src/stdio.mjs`: CLI
- `src/http.mjs`: exports for the HTTP function
- `../netlify/functions/mcp.mjs`: Netlify Function at `/mcp`
- `server.json`: official MCP registry metadata
License: MIT. © Base Ledger IP LLC.
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: listing tasks, getting detailed scores, getting recommendations, and getting warnings. There is no overlap in functionality; an agent can easily select the right tool based on the need.
All tool names follow a consistent verb_noun pattern: list_tasks, get_scores, recommend_model, get_warnings. The convention is predictable and uniform.
With only 4 tools, the server is well-scoped for its purpose of providing AI model evaluation and recommendations. Each tool earns its place and the count is neither too thin nor too heavy.
The tool set covers the full lifecycle: discovering tasks (list_tasks), comparing models (get_scores), getting a tailored recommendation (recommend_model), and checking warnings (get_warnings). There are no obvious missing operations for this domain.