Insider Radar
REMOTE · INSIDER-RADAR-AGENT.INSIDER-RADAR-AGENT.WORKERS.DEV · 2 COMPONENTS
Lab telemetry market: 25 x402 USDC-paid tools — leaderboard, KOL signals, outcomes, more.
Last scanned (6 minutes ago)
Scores higher than 56% of remote servers
Reachable. Last reached 23 Oct 2026.
Category Finance and Payments
How this component scores in each security and reliability category. Every signal is checked automatically against the live server, and we only credit what we can confirm. How we score → Why this is hard to score →
Endpoint Security46
- The endpoint's TLS certificate is valid, in date, and uses a strong key. View diagnostics → Pass
- Authorisation not fully verified: no authorisation is required to call this server, and 2 tool(s) never declared a destructiveHint. The MCP spec treats an absent hint as destructive by default, so we cannot call this surface safe. See how to fix → View diagnostics → Unverified
- HTTPS check failed: the endpoint is reachable over plaintext HTTP. See how to fix → View diagnostics → Fail
- HSTS check failed: the Strict-Transport-Security header is absent. See how to fix → View diagnostics → Fail
- DNSSEC check failed: this domain isn't protected by DNSSEC. See how to fix → View diagnostics → Fail
Schema Quality & AI Usability70
- AI-judged instruction clarity (good).Pass
- Tool/resource definitions use about 2125 tokens (~78/item across 27 items; 27 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage100
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 27 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 27 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
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.
- 28 Sept 26 0
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 25 Sept 26 0
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 12 Sept 26 0
- Stability: 0.97 → pass security
- 26 Aug 26 0
- We updated how we score, so this day's move reflects our rubric, not a change to the server See what changed → functional
- 14 Aug 26 0
- Stability: unverified → 0.03 ▲ functional
- 13 Aug 26 0
First indexed and scored.
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 23 Oct 2026 · Probed https://insider-radar-agent.insider-radar-agent.workers.dev/sse
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=insider-radar-agent.workers.dev | CN=WE1,O=Google Trust Services,C=US | 10 Oct 2026 | 8 Jan 2027 | ECDSA 256 | ECDSA-SHA256 | e6207480ba932b670eae61966ff7f647 |
| SANs: insider-radar-agent.workers.dev, *.insider-radar-agent.workers.dev | ||||||
| CN=WE1,O=Google Trust Services,C=US (CA) | CN=GTS Root R4,O=Google Trust Services LLC,C=US | 13 Dec 2023 | 20 Feb 2029 | ECDSA 256 | ECDSA-SHA384 | 7ff31977972c224a76155d13b6d685e3 |
| CN=GTS Root R4,O=Google Trust Services LLC,C=US (CA) | CN=GlobalSign Root CA,OU=Root CA,O=GlobalSign nv-sa,C=BE | 15 Nov 2023 | 28 Jan 2028 | ECDSA 384 | SHA256-RSA | 7fe530bf331343bedd821610493d8a1b |
Background: What to check on a remote MCP endpoint →
DNSSEC insecure
Validation of insider-radar-agent.insider-radar-agent.workers.dev. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| dev. | present | 60074 | 8 | Verified |
| workers.dev. | absent | Unsigned (proven) parent-signed NSEC/NSEC3 proves an unsigned delegation |
Authentication No authorisation required
The endpoint answered without asking for a token. Anyone who knows the URL can reach it.
| Result | No authorisation required |
|---|---|
| HTTP status | 200 |
Background: How OAuth 2.1 works in the 2026 MCP spec →
Transports 2 probes
| Transport | URL | Outcome | Status | Location |
|---|---|---|---|---|
| sse | https://insider-radar-agent.insider-radar-agent.workers.dev/sse | Verified | 200 | |
| http (plaintext) | http://insider-radar-agent.insider-radar-agent.workers.dev/sse | Served over HTTP | 200 |
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 →
alerts ~69
Recent lab alerts (drawdowns, retirements, anomalies). Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
alpha_track ~89
Daily equity/PnL histories for the lab's edge-protected bots under stable anonymous handles — real performance, withheld identities; track any handle across snapshots. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
arena ~29
Fleet-wide arena summary of the paper-trading lab (bot counts, venues, aggregate equity). Free teaser.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
calibration ~67
Forecast calibration by venue/category — predicted vs realized. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
congress_tape ~83
Parsed House/Senate PTR trades: member, ticker, type, amounts, filing lag (last 1000). Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
creators ~88
Pump.fun deployer index built forward by our launch listener: 900+ creator wallets with launch histories, venue-aware rug counts, first/last seen. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
crowding ~71
Exposure crowding: where the fleet's directional risk is concentrated. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
divergences ~76
Cross-venue probability divergences (e.g. Kalshi vs Polymarket gaps). Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
fleet_ledger ~86
Fleet-wide daily P&L ledger: totals, venue splits, per-day rows, tape reconciliation — the transparency companion to alpha_track. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
insider_plans ~87
10b5-1 plan detection per insider filing — was the buy pre-scheduled? Per-accession tags plus aggregate plan rates. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
insider_tape ~88
Parsed SEC Form-4 insider buys, newest first: roles, titles, shares, price, value, disclosure lag (last 1000). Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
kol_events ~84
Narrated detector event stream: meta-signals, distribution flips, known-rugger callouts (last 2000 events). Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
kol_froth ~84
Memecoin froth gauge, current level plus full history: human buys/hour, danger share, distribution events, percentile. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
kol_outcomes ~99
Forward-marked signal outcomes WITH a matched control arm: rug rates, checkpoint returns (15m/1h/6h/24h) by risk level and confluence — honesty-graded. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
kol_signals_delayed ~86
Same KOL confluence feed as kol_signals_live with every row at least 45 minutes old — the evaluation tier. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
kol_signals_live ~92
Live KOL confluence buy/exit signals (≤15 min old): distinct human buyers, strength, rug-risk callouts, momentum — the actionable tier. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
kol_stats ~85
Per-wallet outcome statistics for ~300 tracked memecoin KOL wallets — our computed win rates and PnL distributions over observed trades. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
leaderboard ~71
Ranked bot leaderboard with eff_n and the luck-ceiling honesty note. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
optimizer ~65
Optimizer status and recent parameter decisions. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
radar_status ~28
Service status: snapshot freshness, product catalog with per-call prices, and the honesty disclaimer. Free.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
research_pack ~91
Seven research panels in one call: family correlation + effective-n, edge calibration, insider event study, regime joins, econ calendar, weekly digest, daily alpha cards. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
signals ~68
Current signal feed headlines from the lab's signal bus. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
smart_holdings ~84
13F institutional holdings rollup: top-held names, filer roster, per-ticker holder counts (top 300). Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
snipe_outcomes ~86
Launch-snipe paper ledger with a randomized control arm: detect latency, entry efficiency, checkpoint marks per snipe. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
sol_depth ~97
Recorded Kalshi SOL-market L2 depth (top-5 levels per side), previous-day and older only (T+1) — we recorded it live; Kalshi publishes no historical depth. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
solscout_verdicts ~86
Copy-gradeability verdicts for Solana memecoin wallets: composite scores, consistency, retirement calls (candidate lists withheld). Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
wire_clusters ~86
Cross-source evidence clusters joining price anomalies to specific SEC filings (Form 4 accessions, URLs) — graded, windowed, cited. Paper-trading lab telemetry; All figures are simulated paper dollars from a research lab — not investment advice, not live trading, no real positions. Ranked returns are dominated by luck until effective bet counts (eff_n), calibration and crowding say otherwise.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
How do I install the Insider Radar MCP server?
Insider Radar is a hosted endpoint at https://insider-radar-agent.insider-radar-agent.workers.dev/sse, so there is nothing to install locally. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
remote · insider-radar-agent.insider-radar-agent.workers.dev
claude mcp add --transport http mypocketsmells-insider-radar 'https://insider-radar-agent.insider-radar-agent.workers.dev/sse'
What is the Insider Radar MCP server?
Insider Radar is an MCP server listed in the public MCP registry as io.github.mypocketsmells/insider-radar. Lab telemetry market: 25 x402 USDC-paid tools, leaderboard, KOL signals, outcomes, more. This page covers its hosted endpoint (https://insider-radar-agent.insider-radar-agent.workers.dev/sse).
Is the Insider Radar MCP server safe to use?
Insider Radar scores 67 out of 100 on VerifyMCP. 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 Insider Radar MCP server expose?
Insider Radar exposes 27 tools: radar_status, arena, leaderboard, signals, alerts, and 22 more. Their descriptions and schemas cost roughly 2,125 tokens of context every time the server is loaded.
Does the Insider Radar MCP server require authentication?
No. We connected to Insider Radar without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the Insider Radar MCP server still maintained?
Insider Radar is still listed as active in the MCP registry. We last reached this channel on 23 October 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.