io.github.TeleKashOracle/mcp-server
NPM · TELEKASH-MCP-SERVER · SCANNED SEP 20
Prediction market probability oracle. 500+ markets from Kalshi, Polymarket & Metaculus.
Available components
How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. How we score → Why this is hard to score →
Supply Chain Security98
- No malware found by supply-chain analysis.Pass
- No known CVEs affecting this package version or its production dependencies.Pass
- No install/post-install scripts declared.Pass
- 31 of 105 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency45
- Source repository is publicly reachable at the declared URL. View diagnostics → Pass
- Provenance check failed: no build-provenance attestation is published. See how to fix → View diagnostics → Fail
- Clear OSI-approved license (MIT).Pass
- Actively maintained (last published 183 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability68
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 2629 tokens (~175/item across 15 items; 15 tools + 0 resources), over budget; trim descriptions and params. See how to fix → Fail
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management90
- Stability observed for 27 of 30 days with no destabilising changes; credit accrues until the full window elapses.Partial
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 100% of tool parameters carry a description.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 15 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 15 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.Pass
How do I install the io.github.TeleKashOracle/mcp-server server?
io.github.TeleKashOracle/mcp-server runs locally as an npm package, launched with npx -y telekash-mcp-server. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
npm · telekash-mcp-server
claude mcp add telekashoracle-mcp-server -- npx -y telekash-mcp-server
{
"mcpServers": {
"telekashoracle-mcp-server": {
"command": "npx",
"args": [
"-y",
"telekash-mcp-server"
]
}
}
} {
"servers": {
"telekashoracle-mcp-server": {
"command": "npx",
"args": [
"-y",
"telekash-mcp-server"
]
}
}
} codex mcp add telekashoracle-mcp-server -- npx -y telekash-mcp-server
{
"$schema": "https://opencode.ai/config.json",
"mcp": {
"telekashoracle-mcp-server": {
"type": "local",
"command": [
"npx",
"-y",
"telekash-mcp-server"
],
"enabled": true
}
}
} openclaw mcp add telekashoracle-mcp-server --command npx --arg -y --arg telekash-mcp-server
mcp_servers:
telekashoracle-mcp-server:
command: "npx"
args: ["-y", "telekash-mcp-server"] {
"McpServers": {
"telekashoracle-mcp-server": {
"Transport": "stdio",
"Command": "npx",
"Arguments": [
"-y",
"telekash-mcp-server"
]
}
}
} assistant mcp add telekashoracle-mcp-server -t stdio -c npx -a -y telekash-mcp-server
{
"mcpServers": {
"telekashoracle-mcp-server": {
"command": "npx",
"args": [
"-y",
"telekash-mcp-server"
]
}
}
} Every change we have recorded for this component, newest first. Security-relevant changes are always shown. ▲ marks a change for the better, ▼ a change for the worse; unmarked changes are neutral.
- 19 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 83 to 87. That category is still filling its 30-day observation window: 25 days of observed history at the previous scan, 26 at this one. The score rises as the window fills, whether or not the server changes.
- 17 Sept 26 −3
- Stability: pass → 0.80 functional
- 16 Sept 26 +1
- Stability: 0.97 → pass security
- 14 Sept 26 +1
No change was recorded against any check on this day. Stability & Change Management went from 90 to 93. That category is still filling its 30-day observation window: 27 days of observed history at the previous scan, 28 at this one. The score rises as the window fills, whether or not the server changes.
- 13 Sept 26 0
- Security disclosure: unverified → fail ▼ functional
- 12 Sept 26 +1
- Security disclosure: fail → unverified ▼ functional
- 10 Sept 26 −3
- Stability: pass → 0.80 functional
- 9 Sept 26 +1
- Stability: 0.97 → pass security
Diagnostic detail from the automated scan of this channel: what the scanner observed at each step, so you can see exactly where a check passed or failed. It is informational only and never changes the trust score.
Captured 20 Sept 2026 · Analysed npm/telekash-mcp-server@0.6.1
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | npm |
Background: How many MCP packages publish verified provenance →
Dependencies 105 packages
| Packages resolved | 105 |
|---|---|
| Stale | 31 |
| Tree resolution | Complete |
Background: SBOMs and build attestations, explained →
The tools this component advertises to a client, with an estimated token cost for each. Expand a tool to see its parameters and schema. The per-tool counts are indicative and are not scored directly; the schema's total context footprint is one signal in Schema Quality & AI Usability. A tool's description is untrusted text the model reads on every call, which is what makes this list a security surface and not just an inventory: how tool poisoning works →
compare_sources ~124
Compare prediction odds across Kalshi and Polymarket for the same event — find pricing discrepancies. Searches for markets matching your query on both Kalshi (CFTC-regulated) and Polymarket, then shows side-by-side probabilities. Use for arbitrage detection, cross-validating predictions, or understanding how regulated vs unregulated markets price the same event. Returns matched pairs with probability delta and which source is more bullish/bearish.
| Name | Type | Req | Description |
|---|---|---|---|
| query | string | yes | Search query to find matching markets across sources (e.g., 'Trump', 'Bitcoin', 'Fed rate') |
No output schema declared.
No examples provided.
create_market ~254
Create a custom prediction market on TeleKash. Markets are binary YES/NO questions that resolve on a specified date. Markets created via this tool are tagged as "agent-created" and appear alongside Kalshi/Polymarket/Metaculus markets. Other agents can query, predict on, and trade these markets. Requirements: - Clear YES/NO question in the title - Resolution date in the future - Category for discoverability - Resolution criteria (how to determine the outcome) Created markets start with 50/50 odds. Probability moves as predictions come in.
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | yes | Market category |
| closes_at | string | yes | When trading closes (ISO 8601 datetime) |
| creator_id | string | yes | Agent identifier creating this market (used for attribution) |
| description | string | – | Detailed description and context for the market |
| resolution_criteria | string | yes | How the outcome will be determined (e.g., 'Based on CoinGecko BTC price at midnight UTC') |
| resolves_at | string | yes | When the market resolves (ISO 8601 datetime, must be after closes_at) |
| title | string | yes | The prediction question (should be answerable with YES or NO) |
No output schema declared.
No examples provided.
detect_arbitrage ~251
Detect cross-source arbitrage opportunities between Kalshi and Polymarket. Scans all active markets to find events priced differently across regulated (Kalshi) and unregulated (Polymarket) prediction markets. Returns actionable opportunities sorted by spread size, with buy/sell signals for each side. Academic research shows $40M+ extracted from prediction market mispricings annually. Cross-source spreads are structural — different regulation, user bases, and liquidity create persistent pricing gaps. Use when looking for: - Arbitrage opportunities between prediction market exchanges - Mispriced markets where consensus disagrees across sources - Risk-free profit opportunities from cross-source spread trading Example: If Kalshi prices "BTC $200K" at 35% and Polymarket at 28%, that's a 7% spread — buy YES on Polymarket, sell YES on Kalshi.
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | – | Filter by category (default: all) |
| limit | number | – | Maximum arbitrage opportunities to return (default: 10, max: 25) |
| min_spread | number | – | Minimum probability spread percentage to flag as arbitrage (default: 5, range: 1-50) |
No output schema declared.
No examples provided.
get_divergences ~202
Find markets where prediction sources disagree — the highest-value signal in forecasting. When Kalshi, Polymarket, and Metaculus show different probabilities for the same event, at least one source is wrong. This tool finds those disagreements, ranked by spread size. Returns: - Markets with the largest cross-source probability gaps - Which source says what - Forecaster count from Metaculus (crowd wisdom depth) - Divergence classification: STRONG (>15%), MODERATE (8-15%), WEAK (3-8%) These are the markets where alpha exists. When sources converge, the edge disappears.
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | – | Filter by category (crypto, politics, economics, sports, weather, other) |
| limit | number | – | Number of divergences to return (default: 10, max: 50) |
| min_spread | number | – | Minimum probability spread to include (default: 5 = 5%) |
No output schema declared.
No examples provided.
get_edge ~227
Capital efficiency analysis — find markets with the best risk/reward for a given bankroll. Uses Kelly Criterion to compute optimal position sizes and expected value. Returns markets ranked by edge (expected profit per dollar risked). For each market: - Edge = your estimated probability minus market probability - Kelly fraction = optimal % of bankroll to allocate - Expected value per dollar risked - Risk classification (conservative/moderate/aggressive) Use this when an agent has limited capital and needs to maximize expected returns. Pairs with get_signal for probability estimates and track_prediction for accuracy tracking.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_id | string | – | Agent ID — uses your prediction history to estimate your edge (optional but recommended) |
| bankroll | number | – | Total capital available for allocation (in dollars, default: 1000) |
| category | string | – | Filter by category |
| limit | number | – | Number of opportunities to return (default: 10, max: 30) |
| min_confidence | string | – | Minimum confidence grade to include (HIGH, MEDIUM, LOW — default: MEDIUM) |
No output schema declared.
No examples provided.
get_history ~120
Get historical probability changes and trend data for a prediction market over time. Returns probability snapshots showing how odds, sentiment, and market consensus have shifted over 1h, 24h, 7d, or 30d. Use for trend analysis, momentum detection, volatility assessment, and understanding how predictions evolve. Essential for backtesting strategies, identifying probability swings, and spotting market-moving events.
| Name | Type | Req | Description |
|---|---|---|---|
| market_id | string | yes | The market UUID or external_id (ticker) |
| timeframe | string | – | Time range for history (default: 24h) |
No output schema declared.
No examples provided.
get_market_stats ~68
Get aggregate statistics across all prediction markets — totals, categories, sources, and volume. Returns total market count, active markets, category distribution, source breakdown (Kalshi vs Polymarket), and aggregate trading volume. Use for market overview, portfolio allocation decisions, or understanding the prediction market landscape.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
get_performance ~135
Get prediction performance metrics for an agent. Shows accuracy, Brier score, calibration, and prediction history. Returns: - Total predictions and resolution rate - Accuracy (% correct) - Brier score (0 = perfect, 1 = worst — lower is better) - Calibration curve (predicted probability vs actual outcome rate) - Recent predictions with outcomes Use this to evaluate an agent's forecasting ability or track your own improvement over time.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_id | string | yes | The agent identifier to check performance for |
| limit | number | – | Number of recent predictions to return (default: 20, max: 100) |
No output schema declared.
No examples provided.
get_probability ~204
Get real-time probability, odds, and likelihood for any prediction market outcome. Returns YES/NO probabilities (0-100%), trading volume, liquidity depth, and market metadata from Kalshi (CFTC-regulated) and Polymarket. Use this when asked about chances, odds, likelihood, forecasts, or predictions for any event — elections, crypto prices, sports, economics, weather, entertainment. Example queries: - "What are the odds Trump wins 2028?" → election forecasting - "What's the probability BTC hits $200K?" → crypto price prediction - "Will the Fed cut rates?" → economic forecasting, interest rates - "What's the chance of rain in NYC?" → weather betting - "Who will win the Super Bowl?" → sports odds
| Name | Type | Req | Description |
|---|---|---|---|
| market_id | string | – | The market UUID or external_id (ticker) to query |
| query | string | – | Natural language query to search for a market (alternative to market_id) |
No output schema declared.
No examples provided.
get_sentiment ~104
Get AI-powered sentiment analysis, recommendation, and confidence score for any prediction market. Returns sentiment score (-1 to 1), actionable recommendation (bullish/bearish/neutral), and AI confidence level. Goes beyond raw probability — analyzes market psychology, crowd wisdom, and directional bias. Use for trade signals, contrarian analysis, or augmenting your own prediction models with market sentiment data.
| Name | Type | Req | Description |
|---|---|---|---|
| market_id | string | yes | The market UUID or external_id (ticker) |
No output schema declared.
No examples provided.
get_signal ~214
Get a structured pre-computed trading signal for any prediction market — TeleKash Probability Format (TPF). Combines probability, confidence, sentiment, noise filter, and cross-source data into one actionable signal. This is the complete intelligence package for autonomous agents. Returns: - probability with confidence grade (HIGH/MEDIUM/LOW/VERY_LOW) - sentiment score with recommendation (bullish/bearish/neutral) - noise filter (signal/weak/noise) — is this momentum real or random walk? - cross-source spread (if market exists on multiple exchanges) - actionable verdict: STRONG_BUY / BUY / HOLD / SELL / STRONG_SELL / NO_SIGNAL Use this as the single entry point when an agent needs to make a trade decision. One call replaces get_probability + get_sentiment + get_history + compare_sources.
| Name | Type | Req | Description |
|---|---|---|---|
| market_id | string | – | The market UUID or external_id (ticker) |
| query | string | – | Natural language query to find the market (alternative to market_id) |
No output schema declared.
No examples provided.
get_trending ~139
Get prediction markets with the biggest probability swings — momentum detection for trending events. Finds markets where odds moved most in the last 1h, 24h, 7d, or 30d. Surfaces breaking events, sentiment shifts, and market-moving news. Use when looking for actionable opportunities, volatile markets, or events where consensus is rapidly changing. Returns markets ranked by absolute probability change with direction (up/down) and current odds.
| Name | Type | Req | Description |
|---|---|---|---|
| limit | number | – | Maximum markets to return (default: 10, max: 25) |
| timeframe | string | – | Lookback window for detecting swings (default: 24h) |
No output schema declared.
No examples provided.
list_markets ~227
Browse and discover prediction markets across 7 categories with filtering and sorting. Lists active betting markets from Kalshi, Polymarket, and Metaculus. Filter by category, sort by trading volume, probability, or closing date. 500+ markets available. Categories: sports, crypto, politics, economics, pop_culture, weather, other. Use when exploring what predictions are available, finding trending markets, or discovering betting opportunities. Example queries: - "Show me crypto prediction markets" → Bitcoin, Ethereum, altcoin forecasts - "What sports markets are trending?" → NFL, NBA, soccer odds - "List political predictions" → elections, legislation, geopolitics - "What economic forecasts are available?" → GDP, inflation, interest rates
| Name | Type | Req | Description |
|---|---|---|---|
| category | string | – | Filter by category (default: all) |
| limit | number | – | Maximum markets to return (default: 10, max: 50) |
| sort_by | string | – | Sort order (default: volume) |
| source | string | – | Filter by data source (default: all) |
No output schema declared.
No examples provided.
search_markets ~192
Search 500+ prediction markets by keyword, topic, or natural language query. Full-text search across Kalshi, Polymarket, and Metaculus. Finds markets matching any topic — politics, crypto, sports, economics, entertainment, science, technology, weather. Returns matching active markets sorted by relevance and trading volume. Use when looking for specific predictions, events, or outcomes to bet on. Example queries: - "Trump election 2028" → presidential race odds - "Bitcoin price prediction" → BTC price target markets - "Super Bowl winner" → NFL championship odds - "AI regulation" → technology policy predictions - "Fed interest rate" → monetary policy forecasts
| Name | Type | Req | Description |
|---|---|---|---|
| limit | number | – | Maximum results to return (default: 10, max: 50) |
| query | string | yes | Search query (e.g., 'Trump', 'Bitcoin', 'Super Bowl') |
No output schema declared.
No examples provided.
track_prediction ~168
Record a prediction for performance tracking. Agents can log their predictions and later check accuracy via get_performance. Records: which market, predicted outcome (YES/NO), predicted probability, and confidence level. When the market resolves, your Brier score and calibration are computed automatically. Use this to build a track record. Agents with verified accuracy get higher trust scores.
| Name | Type | Req | Description |
|---|---|---|---|
| agent_id | string | yes | Your agent identifier (any string — use consistently across predictions) |
| market_id | string | yes | The market UUID or external_id |
| predicted_outcome | string | yes | Your predicted outcome |
| predicted_probability | number | yes | Your estimated probability (0.0-1.0) that YES wins. Required for Brier score. |
| reasoning | string | – | Brief reasoning for the prediction (optional) |
No output schema declared.
No examples provided.
What is the io.github.TeleKashOracle/mcp-server server?
io.github.TeleKashOracle/mcp-server is listed in the public MCP registry as io.github.TeleKashOracle/mcp-server. Prediction market probability oracle. 500+ markets from Kalshi, Polymarket & Metaculus. This page covers its npm package (telekash-mcp-server).
Is the io.github.TeleKashOracle/mcp-server server safe to use?
io.github.TeleKashOracle/mcp-server scores 81 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 20 September 2026. It declares no install or post-install scripts. That is a record of what we were able to check automatically, not an endorsement. The category breakdown on this page shows every signal behind the number, including the ones we could not confirm.
What tools does the io.github.TeleKashOracle/mcp-server server expose?
io.github.TeleKashOracle/mcp-server exposes 15 tools: get_probability, list_markets, get_history, search_markets, get_sentiment, and 10 more. Their descriptions and schemas cost roughly 2,629 tokens of context every time the server is loaded.
Is the io.github.TeleKashOracle/mcp-server server still maintained?
io.github.TeleKashOracle/mcp-server is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.
What licence is the io.github.TeleKashOracle/mcp-server server under?
io.github.TeleKashOracle/mcp-server declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.