Llm Prices Data vs Sigrank MCP — MCP Server Comparison | AllMCPs
Side-by-Side Model Context Protocol Comparison
Llm Prices Data vs Sigrank MCP
In-depth architectural comparison of the Llm Prices Data and Sigrank 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
Llm Prices Data
Finance & Fintech · Local stdio
Quality: 63/100 (Good) | Auth: No auth required
Sigrank MCP
Finance & Fintech · Local stdio
Quality: 64/100 (Good) | Auth: other
Verdict Summary: Choose Llm Prices Data if you need specialized Finance & Fintech tools running via a local process. Choose Sigrank MCP if your workspace requires Finance & Fintech integration with local subprocess execution. Both servers can be configured concurrently in your client's mcpServers manifest.
Which MCP Server Should You Choose?
Choose Llm Prices Data when:
You need dedicated capabilities in the Finance & Fintech domain.
You prefer local stdio subprocess transport architecture.
Your security boundary fits: No auth required (Free / Open Source).
Live LLM API pricing from modelpricewatch.com — current token prices, model comparisons, cheapest-model lookups, and The LLM Price Index across 150+ models from 20+ providers, re-verified daily against official provider pages. No API key required. npx -y @modelpricewatch/mcp
Operator leaderboard measuring users, not models — ranks AI coding operators by token cascade efficiency (Yield = Cache Reads × Output / Input²). 15 MCP tools: rank, pull, submit (ed25519-signed), diagnose, simulate. Privacy-first: only token counts leave the machine. npx sigrank
Category & Scope
Tools & Capabilities Breakdown
Llm Prices Data Tools (5)
search_models
Search the live LLM pricing database by model name, provider, or id. Returns matching models with current input/output prices (USD per 1M tokens), context window, modality, and category. Use this to answer 'how much does <model> cost' or 'what models does <provider> offer'.
get_model_pricing
Get full pricing and capability details for one model by its id (from search_models). Returns input/output/cached price per 1M tokens, blended cost, context window, modality, release date, and the modelpricewatch.com page URL.
compare_models
Compare 2–5 models side by side on price, context window, and capabilities, with a verdict on which is cheapest for input, output, and a typical blended workload.
cheapest_models
Find the cheapest current models, ranked by input price, output price, or a blended cost. The generic ranking covers generative text models (embeddings, OCR and realtime models are excluded — they price different work); pass category to rank a specific pool instead, e.g. 'embedding'. Use to answer 'what is the cheapest model for <use case>'.
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).
Llm Prices Data is categorized under Finance & Fintech and uses a local stdio subprocess. In contrast, Sigrank MCP belongs to Finance & Fintech using local stdio subprocess. Select Llm Prices Data when you need capabilities focused on finance & fintech and Sigrank MCP when you require tools for finance & fintech.
List all tracked AI model providers (OpenAI, Anthropic, Google, etc.) with a short description and their pricing page.
Sigrank MCP Tools (25)
rank_paste
Computes the SigRank yield cascade from a paste of token counts. Parses the input, runs the full cascade math locally (no network calls), and returns: yield (Υ, the headline efficiency metric, Υ = Cache Reads × Output / Input²), snr (signal-to-noise ratio), leverage (Cr/I = cache reads divided by input), velocity (O/I = output divided by input), dev10x (10xDEV score), class (operator experience stage — 24 stages = 8 tiers × 3 sub-stages I/II/III, e.g. ARCH+ I, REFINER II, IGNITER III, or UNCLASSED for empty input), mode (detected working mode), and a deterministic prose "card" summarizing the result in plain English. Accepts two input formats: (1) JSON object {"input":N,"output":N,"cacheCreate":N,"cacheRead":N} or (2) four whitespace-separated numbers in order: input output cacheCreate cacheRead. Returns an error if the input is malformed or has negative values. Use this for a quick one-off ranking without submitting to the board. Do NOT use this to submit your score — use submit_paste instead, which both ranks and publishes. Do NOT use this if you want to rank all four time windows at once — use rank_windows for that. After calling this, use submit_paste to publish the result if you want to appear on the leaderboard.
get_sigrank_standard_record
Build Upsilon's portable sigrank/0.1-draft compatibility record from available token telemetry. Input and output are required; unavailable cache telemetry remains null. Computes the canonical cascade locally through token-cascade and returns Yield, Leverage, Velocity, SNR, and 10xDEV. Upsilon is the measurement product; SigRank is the public leaderboard. No data is submitted or persisted.
get_leaderboard
Fetches the live public SigRank leaderboard from signalaf.com. Reads all ranked operators sorted by yield (Υ = Cache Reads × Output / Input²) and returns an array of operator summaries. Each entry contains: codename (public display name), yield (Υ, the headline efficiency metric), leverage ratio (Cr/I = cache reads divided by input), velocity (O/I = output divided by input), class tier (one of 24 experience stages: 8 tiers × 3 sub-stages, e.g. ARCH+ I, REFINER II, IGNITER III), and rank position (integer, 1-based). Returns an empty array if no operators have submitted yet. Use this to see where operators stand overall, to find specific codenames for get_operator lookups, or to display the current rankings. Do NOT use this to check your own rank if you already know your codename — use get_operator instead for a single-operator profile with per-window breakdowns. After calling this, follow up with get_operator to get detailed metrics for any operator of interest.
get_operator
Fetches one operator's live profile from the SigRank board by their codename. Reads the operator's current submission data from signalaf.com and returns their detailed metrics: yield (Υ), leverage ratio (Cr/I), velocity (O/I), class tier (one of 24 experience stages: 8 tiers × 3 sub-stages, e.g. ARCH+ I, REFINER II, IGNITER III), rank position (integer, 1-based), and per-window breakdowns for each time range (7d, 30d, 90d, all-time) with the four canonical pillars (input, output, cacheCreate, cacheRead) per window. Returns an error if the codename is not found on the board. Use this to look up any operator who has submitted to the board — codenames are public and visible on the leaderboard. Do NOT use this to browse all operators — use get_leaderboard for that. After calling this, you can use simulate_change to model what would happen if the operator adjusted their token mix.
submit_paste
Ranks a paste of token counts locally and shows the cascade result (yield, leverage, velocity, class, card). This is a PREVIEW-ONLY tool — it does not publish to the board. The board's /api/v1/ingest-paste endpoint now requires an authenticated Supabase session, which MCP tools do not carry. To publish to the leaderboard, use submit_verified (which signs and posts to /api/v1/snapshots via the enrolled-device path) or submit directly through the signalaf.com web UI. Use this when you have token counts from ccusage or a dashboard and want to see your score instantly. Do NOT use this if you want to pull your local usage automatically — use tokenpull_submit for the zero-paste flow. Do NOT use this for multi-window dashboard pastes — use rank_windows to rank them first.
tokenpull
Pull your LOCAL token usage from the platform's session logs and rank it across the four windows (7d/30d/90d/all-time) with the cascade — zero paste. Token-only: reads usage counts not message content. The numbers stay on your machine unless you submit them. Some platforms may have partial data (estimated=true when cacheCreate isn't available) or a dataGap note when the log format doesn't expose raw token counts.
tokenpull_submit
Pull your LOCAL token usage from session logs and compute the cascade per window — the zero-paste preview flow. Reads the four canonical pillars (input, output, cacheCreate, cacheRead) per window from your local logs and computes yield, leverage, velocity, class, and card. This is a PREVIEW-ONLY tool — it does not publish to the board. The board's /api/v1/ingest-paste endpoint now requires an authenticated Supabase session, which MCP tools do not carry. To publish to the leaderboard, use submit_verified (which signs and posts to /api/v1/snapshots via the enrolled-device path) or submit directly through the signalaf.com web UI. Token-only — no prompt content is read or transmitted.
rank_windows
Rank all four time windows (7d/30d/90d/all-time) in one call from a dashboard paste — paste the full table from ccusage, tokscale, or the Claude Max usage dashboard and get the cascade (Υ, SNR, Leverage, Velocity, 10xDEV, class, card) for each window. Each window is parsed and scored independently. Named keys required (input/output/cacheCreate/cacheRead); positional order is NOT safe here (dashboards list cache_read before cache_create — see WINDOWED_PROFILES gotcha). Omit windows you don't have — partial input is allowed (1–4 windows). Does NOT submit to the board; use tokenpull_submit for a local zero-paste preview, or submit_verified to publish via the enrolled-device path.
watch_tokenpull
One poll per call: pulls your local token logs and returns the current cascade for the watched window — the tool never blocks or loops. Re-call at your desired cadence to watch for changes (interval_s is advisory only and echoed back as poll_interval_s). With submit:true (and an enrolled device) each call may also sign + publish the watched window to the board, rate-limited to once per 5 min per platform+window; default is preview-only (no submit).
tokenpull_compare
Pull token usage from ALL four local sources in parallel — tokenpull (JSONL canon), ccusage CLI, token-dashboard SQLite, and tokscale report — and return them side-by-side with delta % vs tokenpull as the baseline. Also computes the cascade (Υ, SNR, Leverage, class) for each source so you can see how each verifier scores. Useful for validating your numbers before submitting, or understanding discrepancies between tools. Claude only for token-dash; codex and others use tokenpull + ccusage + tokscale. Token-only, on-device.
enroll
Bind THIS device to your SigRank operator so your signed token runs cascade to the live board. Paste the key from signalaf.com → Settings → "New key" (or "Generate connect code"). On first run it generates + stores a local ed25519 keypair (~/.sigrank-mcp/identity.json); only the PUBLIC key is ever sent. By enrolling you agree to the SignalAF Terms of Service (signalaf.com/terms) and Privacy Policy (signalaf.com/privacy). Need a new key? Click "New key" at signalaf.com → Settings, then paste it here.
submit_verified
Publish your LOCAL token runs to the SigRank board as a VERIFIED operator — the enrolled, signed path. Reads your pillars (tokenpull), builds the canonical Schema 1.0 snapshot per window, ed25519-signs it with your device key, and POSTs to /api/v1/snapshots. Requires `npx sigrank-mcp enroll` first (a bound device). Only signed submissions from a trusted device rank on the board. Token-only; the private key never leaves your machine. Pass dry_run:true to inspect the exact signed payload without publishing.