Deepfake detection, media intelligence, and invisible watermarking for audio, image, and video via the Resemble AI API, plus docs tools. Remote MCP server (Streamable HTTP) — also published in the official MCP registry as io.github.resemble-ai/resemble-mcp.
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Average 3.5/5 across 7 of 7 tools scored. Lowest: 2.7/5.
Each tool has a clearly distinct purpose: media analysis, watermarking, deepfake detection, watermark detection, detection retrieval, question answering about detections, and audio source tracing. No two tools overlap in functionality.
Tool names consistently use lowercase with underscores and a verb_noun pattern, though 'ask_about_detection' is slightly longer and deviates from the direct verb_noun structure (e.g., 'detect_deepfake'). Overall, the pattern is predictable.
Seven tools effectively cover the core capabilities of the Resemble AI server (media analysis, deepfake detection, watermarking, source tracing, and result queries). The count feels well-scoped for the domain.
The tool set covers the main workflows: detection, analysis, watermarking, and retrieval. Minor gaps exist, such as lacking a tool to list all detections or delete media/watermark, but the essential operations are present.
analyze_mediaBInspectAnalyze media for structured intelligence: transcription, translation, language, speaker info, emotion, scene description, abnormalities, and misinformation analysis. media_type: auto | audio | video | image.
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| media_type | No | auto | |
| structured_json | No | ||
| max_wait_seconds | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so the description carries the burden. It lists outputs but does not disclose potential side effects, authentication needs, or processing behavior (e.g., asynchronous nature, error handling), leaving some gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (two sentences) with no filler, but the first sentence could be better structured (e.g., grouping related outputs). Still efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema and low parameter documentation make the description incomplete for an agent to fully understand tool behavior and response format, especially for a 4-param tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description only clarifies media_type (auto/audio/video/image) but does not explain the required 'url' parameter or optional 'structured_json' and 'max_wait_seconds', leaving most parameters undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool analyzes media for structured intelligence and lists specific outputs (transcription, translation, etc.), distinguishing it from siblings that focus on watermarking and deepfake detection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies use for general media analysis but lacks explicit when-to-use or when-not-to-use guidance, nor does it mention alternatives or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
apply_watermarkCInspectEmbed an invisible Resemble provenance watermark into media (audio-first) and return the watermarked media URL. strength 0.0-1.0 (image/video only).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| strength | No | ||
| custom_message | No | ||
| max_wait_seconds | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose all behavioral traits. It mentions embedding and returning a URL, but omits crucial details: whether the original is modified, what happens to existing watermarks, synchronous vs asynchronous behavior, authentication needs, or error handling. The 'audio-first' qualifier is vague.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise (one sentence plus parenthetical) and front-loaded with purpose. However, it sacrifices completeness, missing key parameter details and behavioral notes, which makes it less effective.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with 4 parameters, no annotations, and no output schema, the description is insufficient. It does not explain the return type, async behavior (implied by max_wait_seconds), limitations, or parameter interactions. The 'audio-first' note is unclear.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Only one parameter (strength) is partially described with a range and scope constraint. Three parameters (url, custom_message, max_wait_seconds) receive no explanation. Given 0% schema description coverage, the description should explain all parameters but fails to do so.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's function: embedding an invisible Resemble provenance watermark into media, with 'audio-first' specified and a note about strength for image/video. It distinguishes from sibling tools like detect_watermark or trace_audio_source.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No explicit guidance on when to use this tool versus alternatives. While implied for embedding watermarks, no exclusions or comparisons to siblings are provided, leaving the agent to infer usage.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
ask_about_detectionBInspectAsk a natural-language question about a COMPLETED detection (e.g. 'how confident is the model that this is fake?'). Returns the grounded answer.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| detect_uuid | Yes | ||
| max_wait_seconds | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears full burden for behavioral disclosure. It fails to mention key traits: side effects (none expected, but not stated), authentication needs, rate limits, or what happens on timeout (max_wait_seconds). The constraint 'completed detection' is useful but insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with an example, which is concise and front-loaded. However, it lacks structure (e.g., broken into purpose, input, output) and the example is embedded rather than separated. It earns its place but could be more organized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no output schema, and no annotations, the description is too brief. It does not explain the return format (e.g., plain text, structured JSON), how 'grounded answer' relates to the detection data, or how max_wait_seconds affects the call. A more complete description would include these details.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must explain all parameters. It covers detect_uuid and query implicitly via the example and phrasing, but completely omits max_wait_seconds—its meaning, default, and behavior. The example provides some context for query format, but not for detect_uuid format.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: ask a natural-language question about a completed detection, with an example query. It distinguishes from sibling tools like detect_deepfake or get_detection by specifying the interaction type (question-answering) and input requirement (completed detection).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage context ('completed detection') but provides no explicit when-to-use or when-not-to-use guidance. It does not mention alternatives among siblings (e.g., analyze_media, get_detection) or prerequisites like detection completion status.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
detect_deepfakeAInspectDetect 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 | Required | Description | Default |
|---|---|---|---|
| url | Yes | ||
| visualize | No | ||
| model_type | No | auto | |
| max_wait_seconds | No | ||
| run_intelligence | No | ||
| use_ood_detector | No | ||
| use_reverse_search | No | ||
| zero_retention_mode | No | ||
| audio_source_tracing | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses polling behavior (async), return values (verdict, confidence, full result), and optional features (e.g., zero-retention mode auto-deletes media). However, it does not discuss error handling, rate limits, or whether the operation is read-only. This is moderate transparency, neither excellent nor poor.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences long, front-loading the core purpose and then detailing the polling behavior and optional flags. It is concise with no redundant information, though the parameter listing could be more structured. It earns its place without being verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 9 parameters and no output schema, the description explains the return values (verdict, confidence, full result) and covers key behaviors (polling, optional features). However, it omits error scenarios, the structure of 'full result', and comparative guidance against siblings. It is adequate but not fully comprehensive for the tool's complexity.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description must compensate. It adds meaning for most parameters: explains flags like run_intelligence ('adds media intelligence'), zero_retention_mode ('auto-delete media'), and lists model_type options. However, it does not mention max_wait_seconds, leaving a gap. Overall, it adds significant value beyond the bare schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Detect whether media (audio, image, or video) at a public HTTPS URL is a deepfake / AI-generated', providing a specific verb (detect) and resource (media) with clear scope (deepfake/AI-generated). It distinguishes itself from sibling tools like detect_watermark or trace_audio_source by focusing on deepfake detection.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage by stating it polls to completion and lists optional features, but it does not explicitly state when to use this tool versus alternatives, nor does it provide exclusions or prerequisites beyond a public URL. The model_type parameter is mentioned but not explained in terms of when to choose each option.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
detect_watermarkAInspectCheck whether media at a public HTTPS URL carries a Resemble invisible watermark (audio-first; per-channel verdict for audio).
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses that the tool checks a public HTTPS URL and gives a per-channel verdict for audio, but does not mention auth needs, rate limits, or behavior on inaccessible URLs.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence that efficiently conveys the main purpose and key constraints. It is concise without being overly terse, though it could be slightly more structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a single-parameter tool with no output schema, the description provides adequate context: what it checks (watermark), media type (audio-first), output format (per-channel verdict), and URL requirement (public HTTPS). It lacks detail on the verdict format but is mostly complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
With 0% schema description coverage, the description adds meaning by specifying the URL must be 'public HTTPS', which is not in the schema (just 'string'). This clarifies the parameter's constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool checks media at a public HTTPS URL for a Resemble invisible watermark, specifying 'audio-first' and 'per-channel verdict for audio', which distinguishes it from siblings like detect_deepfake and apply_watermark.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not explicitly provide guidance on when to use this tool versus alternatives. While the sibling names imply different detection foci, the description lacks direct usage context or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_detectionAInspectFetch a detection by UUID, polling until it completes (bounded). Use after detect_deepfake when a long job exceeded its wait budget.
| Name | Required | Description | Default |
|---|---|---|---|
| uuid | Yes | ||
| max_wait_seconds | No |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries burden. Mentions polling 'until it completes (bounded)' and parameter max_wait_seconds, but doesn't detail timeout behavior, errors, or idempotency.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, no wasted words. Front-loads purpose and behavior, then usage context. Highly efficient.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema, description doesn't mention return value structure. Adequate for 2-param polling tool but missing details on response format and potential errors.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage 0% so description must compensate. Explains 'uuid' as identifier, implies max_wait_seconds as polling bound, but lacks explicit definition of max_wait_seconds beyond default.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Clearly states verb 'Fetch', resource 'detection by UUID', and polling behavior. Distinguishes from siblings by referencing usage after detect_deepfake.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'Use after detect_deepfake when a long job exceeded its wait budget', providing clear context. Lacks explicit when-not but implies alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
trace_audio_sourceAInspectGet 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 | Required | Description | Default |
|---|---|---|---|
| uuid | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must disclose behavior. It mentions the prerequisite condition and the nature of the report (which AI platform). However, it does not describe the output format, error behavior if condition not met, or any side effects, leaving gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences with no wasted words. The first sentence states the purpose, the second adds a crucial condition. It is front-loaded and concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple tool with one parameter and no output schema, the description covers purpose and a key prerequisite. However, it lacks details on the report's contents (beyond platform) and error cases, making it somewhat incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The only parameter 'uuid' has no description in the schema (0% coverage). The description partially clarifies that it is for a detection, but does not explicitly state it is the detection's UUID or provide further formatting details, adding minimal value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool gets an audio source-tracing report for a detection, specifying which AI platform generated the fake audio. It distinguishes from siblings by focusing on source tracing and including a condition for availability.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly says the report is only available when detection ran with audio_source_tracing and labeled the audio fake, providing clear context for use. It does not explicitly compare to alternatives but implies the condition.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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