Resemble AI
REMOTE · MCP.RESEMBLE.AI · 2 COMPONENTS
Resemble AI: deepfake, AI-text, and site agent detection, Detect Agents, intelligence, watermarks.
Available components
Last scanned (1 hour ago)
Scores higher than 64% of remote servers
Reachable. Last reached 5 Nov 2026.
Category AI and Machine Learning
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 Security57
- 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 18 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 is enforced; there's no plaintext access path. View diagnostics → Pass
- 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
Transport & Reachability100
- Verified streamable-http transport via a live MCP handshake. View diagnostics → Pass
Schema Quality & AI Usability69
- AI-judged instruction clarity (good).Pass
- Context-footprint check failed: tool/resource definitions use about 2159 tokens (~119/item across 18 items; 18 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 Management100
- No destabilizing schema changes in the last 30 days.Pass
Tool Coverage67
- 100% of tools have a non-trivial description (not blank, and not just the tool's name).Pass
- 0% of tool parameters carry a description.Fail
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 18 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 19 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.
- 5 Nov 26 +1
- Stability: 0.97 → pass security
- 4 Nov 26 −1
- Stability: pass → 0.97 functional
- 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
- 24 Sept 26 0
- The server rewrote its instructions, which are the text every model session reads security
- Schema quality: pass → fail ▼ functional
- Server version: 1.27.2 → 1.30.0 functional
- New tool “agent_detection_get_analytics” functional
- New tool “agent_detection_get_session” functional
- New tool “agent_detection_list_sessions” functional
- New tool “agent_detection_list_sites” functional
- New tool “agent_detection_update_site” functional
- New tool “detect_ai_text” functional
- New tool “get_detect_agent_run” functional
- New tool “get_text_detection” functional
- New tool “list_detect_agents” functional
- New tool “run_detect_agent_investigation” functional
- New tool “agent_detection_create_site” functional
- 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
- 25 Aug 26 0
- Stability: 0.97 → pass security
- 11 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
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 5 Nov 2026 · Probed https://mcp.resemble.ai/mcp
TLS valid
Negotiated TLS 1.3 with TLS_AES_128_GCM_SHA256 .
| Subject | Issuer | Valid from | Valid until | Key | Signature | Serial |
|---|---|---|---|---|---|---|
| CN=mcp.resemble.ai | CN=WE1,O=Google Trust Services,C=US | 22 Sept 2026 | 21 Dec 2026 | ECDSA 256 | ECDSA-SHA256 | 86aebcba598b713313e790c5cefbd903 |
| SANs: mcp.resemble.ai | ||||||
| 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 mcp.resemble.ai. — Not signed
| Zone | DS | Keys | Algorithms | Outcome |
|---|---|---|---|---|
| . | trust_anchor | 20326, 38696 | 8, 8 | Verified |
| ai. | present | 1814, 3799 | 8, 8 | Verified |
| resemble.ai. | 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 |
|---|---|---|---|---|
| streamable-http | https://mcp.resemble.ai/mcp | Verified | 200 | |
| http (plaintext) | http://mcp.resemble.ai/mcp | HTTPS enforced | 301 | https://mcp.resemble.ai/mcp |
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 →
agent_detection_create_site ~173
Create an Agent Detection integration for a website and return its publishable key (pk_live_...) and the script snippet to install. domain is the site's domain, e.g. example.com. If the team already has an integration for that domain, the existing one is returned instead of a duplicate. The publishable key is safe to put in a public web page: it only sends telemetry, only from the listed domains. Never put the Resemble API key in a page. Paste the snippet into the <head> of every page to cover; the page can then listen for the 'resemble:verdict' event or call ResembleAgentDetection.onVerdict().
| Name | Type | Req | Description |
|---|---|---|---|
| domain | string | yes | – |
| name | string | – | – |
No output schema declared.
No examples provided.
agent_detection_get_analytics ~201
Summarise who is visiting a website: people vs AI agents. Returns totals (visits, decided, agents, people, agent_share, gated), counts per visitor class (human, computer_use_agent, browser_automation, scripted_client), and breakdowns by page, day, and network. Use this to show how much traffic comes from agents and which pages they use, so the site can be made easier for helpful agents and protected from harmful ones. Filters are optional: site_id (one integration), settled ('human' or 'agent'), page_path, and since/until as ISO 8601 times. agent_share is agents / decided visits, or null when nothing is decided yet.
| Name | Type | Req | Description |
|---|---|---|---|
| page_path | string | – | – |
| settled | string | – | – |
| since | string | – | – |
| site_id | – | – | – |
| until | string | – | – |
No output schema declared.
No examples provided.
agent_detection_get_session ~113
Get one website visit and the evidence behind its verdict: the feature vector it was decided from, plain-language observations, and each model read. When the visit was recorded, the raw event stream is available; it is omitted unless include_events is true, and then capped at the first 200 events. The verdict is advisory: it comes from browser signals, which a determined attacker can fake.
| Name | Type | Req | Description |
|---|---|---|---|
| include_events | boolean | – | – |
| session_id | integer | yes | – |
No output schema declared.
No examples provided.
agent_detection_list_sessions ~136
List individual website visits, newest first, each with its verdict (settled: human, agent, or null while still open), visitor_class, p_person, the page, referrer, user agent, and duration. Same filters as agent_detection_get_analytics plus a free-text search. per_page max 100.
| Name | Type | Req | Description |
|---|---|---|---|
| page | integer | – | – |
| page_path | string | – | – |
| per_page | integer | – | – |
| search | string | – | – |
| settled | string | – | – |
| since | string | – | – |
| site_id | – | – | – |
| until | string | – | – |
No output schema declared.
No examples provided.
agent_detection_list_sites ~80
List this team's Agent Detection integrations. Agent Detection tells a website whether each visitor is a person or an AI agent (for example a personal assistant browsing on someone's behalf). Each integration is one website, with the domains its publishable key works on and the one-line script snippet to paste into the site's <head>.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
agent_detection_update_site ~88
Replace the list of domains an Agent Detection integration's publishable key is valid on (for example to add a staging or www host), and optionally rename it. domains replaces the whole list, so include every domain to keep; at least one is required.
| Name | Type | Req | Description |
|---|---|---|---|
| domains | array | yes | – |
| name | string | – | – |
| site_id | integer | yes | – |
No output schema declared.
No examples provided.
analyze_media ~80
Analyze media for structured intelligence: transcription, translation, language, speaker info, emotion, scene description, abnormalities, and misinformation analysis. media_type: auto | audio | video | image.
| Name | Type | Req | Description |
|---|---|---|---|
| max_wait_seconds | integer | – | – |
| media_type | string | – | – |
| structured_json | boolean | – | – |
| url | string | yes | – |
No output schema declared.
No examples provided.
apply_watermark ~77
Embed an invisible Resemble provenance watermark into media (audio-first) and return the watermarked media URL. strength 0.0-1.0 (image/video only).
| Name | Type | Req | Description |
|---|---|---|---|
| custom_message | string | – | – |
| max_wait_seconds | integer | – | – |
| strength | number | – | – |
| url | string | yes | – |
No output schema declared.
No examples provided.
ask_about_detection ~66
Ask a natural-language question about a COMPLETED detection (e.g. 'how confident is the model that this is fake?'). Returns the grounded answer.
| Name | Type | Req | Description |
|---|---|---|---|
| detect_uuid | string | yes | – |
| max_wait_seconds | integer | – | – |
| query | string | yes | – |
No output schema declared.
No examples provided.
detect_ai_text ~223
Detect whether TEXT (an essay, email, post, review, comment, article) was written by an AI language model. Needs at least 25 words: shorter text comes back as prediction 'uncertain' with no model call - concatenate several messages from the same author to reach the minimum. Returns prediction ('ai' | 'human'), confidence (how sure the model is of that prediction; it is NOT an AI probability, so 'human' at 0.95 means strongly human), and the full result. Only the first ~400 words are read; chunk longer documents. A result can take several minutes when the model is cold or jobs are queued - if status is still 'processing' when the wait budget ends, resume with get_text_detection(uuid). Detection is probabilistic, not proof of authorship.
| Name | Type | Req | Description |
|---|---|---|---|
| max_wait_seconds | integer | – | – |
| text | string | yes | – |
| threshold | number | – | – |
| zero_retention_mode | boolean | – | – |
No output schema declared.
No examples provided.
detect_deepfake ~182
Detect whether media (audio, image, or video) at a public HTTPS URL is a deepfake / AI-generated. Polls to completion and returns the verdict label, confidence score, and full result. Optional flags add media intelligence, audio source tracing, visualization, reverse image search (images), out-of-distribution detection, and zero-retention (auto-delete media after analysis). model_type: auto | image | talking_head.
| Name | Type | Req | Description |
|---|---|---|---|
| audio_source_tracing | boolean | – | – |
| max_wait_seconds | integer | – | – |
| model_type | string | – | – |
| run_intelligence | boolean | – | – |
| url | string | yes | – |
| use_ood_detector | boolean | – | – |
| use_reverse_search | boolean | – | – |
| visualize | boolean | – | – |
| zero_retention_mode | boolean | – | – |
No output schema declared.
No examples provided.
detect_watermark ~43
Check whether media at a public HTTPS URL carries a Resemble invisible watermark (audio-first; per-channel verdict for audio).
| Name | Type | Req | Description |
|---|---|---|---|
| url | string | yes | – |
No output schema declared.
No examples provided.
get_detect_agent_run ~66
Fetch a persisted Detect Agent investigation by run_id, including its full event transcript. Use after run_detect_agent_investigation reported timed_out, or to re-read an earlier investigation.
| Name | Type | Req | Description |
|---|---|---|---|
| preset_id | string | yes | – |
| run_id | string | yes | – |
No output schema declared.
No examples provided.
get_detection ~54
Fetch a detection by UUID, polling until it completes (bounded). Use after detect_deepfake when a long job exceeded its wait budget.
| Name | Type | Req | Description |
|---|---|---|---|
| max_wait_seconds | integer | – | – |
| uuid | string | yes | – |
No output schema declared.
No examples provided.
get_text_detection ~57
Fetch a text detection by UUID, polling until it completes (bounded). Use after detect_ai_text returned status 'processing' - typically a cold start.
| Name | Type | Req | Description |
|---|---|---|---|
| max_wait_seconds | integer | – | – |
| uuid | string | yes | – |
No output schema declared.
No examples provided.
list_detect_agents ~62
List the managed Detect Agents — investigators that wrap detection in a multi-step workflow ending in a written assessment. Returns each agent's preset_id plus this team's run allowance (free_runs_remaining, entitled), which is worth checking before starting a run.
Input schema present but exposes no named parameters.
No output schema declared.
No examples provided.
run_detect_agent_investigation ~197
Run a managed Detect Agent investigation against media at a public HTTPS URL and return its verdict. preset_id is one of: investigate_social_content, review_insurance_claim, verify_breaking_news, verify_document, verify_evidence, verify_id (confirm with list_detect_agents). query states the investigation objective; check_urls adds URLs for the agent to check. Consumes a run of the team's allowance. `label`/`score` are the Detect evidence and are the only basis for an authenticity claim; `verdict` is the agent's written assessment. If timed_out is true the investigation is still running server-side — retrieve it later with get_detect_agent_run.
| Name | Type | Req | Description |
|---|---|---|---|
| check_urls | string | – | – |
| max_wait_seconds | integer | – | – |
| preset_id | string | yes | – |
| query | string | – | – |
| url | string | yes | – |
No output schema declared.
No examples provided.
trace_audio_source ~55
Get the audio source-tracing report for a detection (which AI platform generated the fake audio). Only available when detection ran with audio_source_tracing and labeled the audio fake.
| Name | Type | Req | Description |
|---|---|---|---|
| uuid | string | yes | – |
No output schema declared.
No examples provided.
How do I install the Resemble AI MCP server?
Resemble AI is a hosted endpoint at https://mcp.resemble.ai/mcp, 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 · mcp.resemble.ai
claude mcp add --transport http resemble-ai-resemble-mcp 'https://mcp.resemble.ai/mcp'
What is the Resemble AI MCP server?
Resemble AI is an MCP server listed in the public MCP registry as io.github.resemble-ai/resemble-mcp. Resemble AI: deepfake, AI-text, and site agent detection, Detect Agents, intelligence, watermarks. This page covers its hosted endpoint (https://mcp.resemble.ai/mcp).
Is the Resemble AI MCP server safe to use?
Resemble AI scores 75 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 Resemble AI MCP server expose?
Resemble AI exposes 18 tools: detect_deepfake, get_detection, detect_ai_text, get_text_detection, analyze_media, and 13 more. Their descriptions and schemas cost roughly 1,953 tokens of context every time the server is loaded.
Does the Resemble AI MCP server require authentication?
No. We connected to Resemble AI without credentials and it answered, so anything it exposes is reachable by anyone who knows the address.
Is the Resemble AI MCP server still maintained?
Resemble AI is still listed as active in the MCP registry. We last reached this channel on 5 November 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.