ai.nudg3/brand-intelligence
PYPI · NUDG3-MCP · 2 COMPONENTS
Query your Nudg3 brand visibility across ChatGPT, Claude, Gemini, AI Overviews, and Perplexity.
Available components
Last scanned (21 hours ago)
Scores higher than 57% of PyPI servers
Category Marketing and SEO
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 Security50
- Malware scan not yet available for this package.Unverified
- No known CVEs affecting this package version or its production dependencies.Pass
- Runs hatchling.build at install time, a recognised build step with no custom scripting around it. View diagnostics → Pass
- 1 of 31 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency19
- Repository check failed: the declared repository URL returned HTTP 404. See how to fix → View diagnostics → Fail
- 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 156 days ago).Pass
- Security-disclosure policy not yet verified: we couldn't inspect the source repository.Unverified
Schema Quality & AI Usability67
- AI-judged instruction clarity (excellent).Pass
- Context-footprint check failed: tool/resource definitions use about 2574 tokens (~234/item across 11 items; 11 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 Management67
- Stability observed for 20 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
- Structured output schemas are declared (100% of tools); any adoption earns full credit.Pass
Tool Safety100
- No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.Pass
- We read all 11 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 12 captured unit(s) of tool text and found none that tries to manipulate the model reading it.Pass
Capabilities100
- Implements a current MCP spec version (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.
- 3 Nov 26 −5
- Stability: pass → 0.63 functional
- 2 Nov 26 −15
- Malware scan: pass → unverified ▼ security
- 1 Nov 26 +15
- Malware scan: unverified → pass ▲ security
- 30 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 28 Oct 26 +1
- Stability: 0.97 → pass security
- 27 Oct 26 −1
- Stability: pass → 0.97 functional
- 21 Oct 26 +1
- Stability: 0.97 → pass security
- 20 Oct 26 −1
- Stability: pass → 0.97 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 4 Nov 2026 · Analysed pypi/nudg3-mcp@1.1.0
Provenance No attestation
The registry publishes no build provenance for this version, so there is nothing to verify.
| Result | No attestation |
|---|---|
| Ecosystem | pypi |
| Reason | No attestation published |
Background: How many MCP packages publish verified provenance →
Install scripts 1 script
| Hook | Tier | Command |
|---|---|---|
| build_backend | allowlisted | hatchling.build |
Background: Why install scripts are a supply-chain risk →
Dependencies 31 packages
| Packages resolved | 31 |
|---|---|
| Stale | 1 |
| 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 →
analyze_competitors Analyze Competitors ~193
Competitive landscape — how brands rank on discovery (unbranded) queries. Shows position rankings, visibility gaps, and which competitors are gaining or losing ground. "Discovery" means unbranded category queries where brands compete on relevance, not brand recognition (e.g., "best AI monitoring tools"). Negative-sentiment classifications in the ranking are guaranteed by v1.14 persistence guards to be grounded in >= 1 negative indicator phrase (no separate quality knob needed — the data layer enforces it).
| Name | Type | Req | Description |
|---|---|---|---|
| date_range | string | – | "7d", "30d", "90d", or "YYYY-MM-DD:YYYY-MM-DD" |
| providers | – | – | Filter by AI provider names (e.g. ["ChatGPT", "Gemini"]) |
| workspace_id | – | – | Optional. UUID of the workspace to target. Required only for agency/company keys with more than one workspace accessible. |
Structured output declared, but exposes no named fields.
No examples provided.
analyze_prompts Analyze Prompts ~285
Prompt-level performance grouped by funnel stage. Shows which tracking prompts perform best/worst for a specific brand. visibility_type controls the analysis scope: - "organic" (default): Discovery/unbranded prompts only — the true competitive metric. Branded prompts are excluded because they inflate scores artificially. - "branded": Research + purchase prompts — what AI says when asked about you by name. - "all": All prompts regardless of tagging. Funnel stages explained: - Discovery: unbranded category queries ("best AI monitoring tools") - Research: evaluation/comparison queries ("Nudg3 vs Semrush") - Purchase: buy-intent queries ("Nudg3 pricing")
| Name | Type | Req | Description |
|---|---|---|---|
| brand_id | – | – | Optional. UUID of the brand to analyze. If omitted, the workspace's primary brand is auto-resolved — call this tool without brand_id when the user just asks about "our prompts" or "we" without naming… |
| date_range | string | – | "7d", "30d", "90d", or "YYYY-MM-DD:YYYY-MM-DD" |
| providers | – | – | Filter by AI provider names |
| visibility_type | string | – | "organic" (default), "branded", or "all" |
| workspace_id | – | – | Optional. UUID of the workspace. Required only for agency/company keys with >1 workspace. |
Structured output declared, but exposes no named fields.
No examples provided.
analyze_responses Analyze Responses ~314
Read what AI models actually say — provider-grouped response samples. Default mode groups AI responses by provider with truncated text, sentiment scores, brand mention patterns, and co-mention detection. When `group_by` is supplied, the tool returns a cross-tab aggregation instead — useful for "Coca-Cola visibility by sub-sector x engine" style questions in one call. Supported dimensions: "provider", "category" ("tag" is accepted as an alias for "category"). Note: the cross-tab is aggregated over a sample of up to 100 matching responses, not the full dataset (server-side full-population aggregation is a Phase 2 follow-up — the result includes `is_sample` and `warning` fields).
| Name | Type | Req | Description |
|---|---|---|---|
| date_range | string | – | "7d", "30d", "90d", or "YYYY-MM-DD:YYYY-MM-DD" |
| group_by | – | – | Optional list of dimensions to cross-tab on. Examples: ["provider"], ["category"], ["category","provider"]. |
| limit | integer | – | Max responses for default (non-group_by) mode. Default 15, max 20. Ignored when group_by is set (sample is always sized at 100). |
| providers | – | – | Filter by AI provider names (e.g. ["ChatGPT"]) |
| tags | – | – | Filter by funnel stage (e.g. ["discovery"]) |
| workspace_id | – | – | Optional. UUID of the workspace. Required only for agency/company keys with >1 workspace. |
Structured output declared, but exposes no named fields.
No examples provided.
analyze_sources Analyze Sources ~138
Citation source analysis — which websites AI models cite. Shows top cited domains, source type distribution (reference, social, news, review, commerce), content gaps (missing source types), and competitor domains appearing in your brand's AI responses.
| Name | Type | Req | Description |
|---|---|---|---|
| date_range | string | – | "7d", "30d", "90d", or "YYYY-MM-DD:YYYY-MM-DD" |
| providers | – | – | Filter by AI provider names |
| tags | – | – | Filter by funnel stage (e.g. ["discovery", "research"]) |
| workspace_id | – | – | Optional. UUID of the workspace. Required only for agency/company keys with >1 workspace. |
Structured output declared, but exposes no named fields.
No examples provided.
export_data Export Data ~180
Export raw workspace data as CSV. Use this only when the user wants raw data for spreadsheets or BI tools. For analysis, use the investigation tools (get_overview, analyze_*) instead. Datasets: chat_responses, sources_citations, dashboard_graph, prompts_mentions. Requires the export:data scope (Professional tier or above).
| Name | Type | Req | Description |
|---|---|---|---|
| confirm_large_export | boolean | – | Set true to proceed with exports over 10,000 rows |
| dataset | string | yes | One of: chat_responses, sources_citations, dashboard_graph, prompts_mentions |
| date_from | string | yes | Start date (YYYY-MM-DD) |
| date_to | string | yes | End date (YYYY-MM-DD) |
| providers | – | – | Filter by provider names |
| workspace_id | – | – | Optional. UUID of the workspace. Required only for agency/company keys with >1 workspace. |
Structured output declared, but exposes no named fields.
No examples provided.
get_actions Get Actions ~259
List or retrieve workspace actions derived from report insights. Actions are recommendations that have been created from AI-generated insights or manually. Each action tracks status, priority, owner team, and links back to the source report. Two modes: - List: returns paginated actions with filters (default) - Detail: pass action_id for full action with context and brief info Requires read:insights scope (Professional tier or above).
| Name | Type | Req | Description |
|---|---|---|---|
| action_id | – | – | UUID of a specific action (returns full detail with context) |
| owner_team | – | – | Filter: "tech", "content", "marketing", or "leadership" |
| page | integer | – | Page number for list mode |
| per_page | integer | – | Results per page (max 50) |
| priority | – | – | Filter: "high", "medium", or "low" |
| recommendation_type | – | – | Filter: "opportunity", "threat", "quick_win", or "prompt_edit" |
| source_report_id | – | – | Filter by originating report UUID |
| status | – | – | Filter: "pending", "in_progress", "ready", "completed", or "dismissed" |
| workspace_id | – | – | Optional. UUID of the workspace. Required only for agency/company keys with >1 workspace. |
Structured output declared, but exposes no named fields.
No examples provided.
get_insights Get Insights ~199
Get AI-generated actionable insights from a visibility report. Returns categorized recommendations: opportunities (gaps to exploit), threats (competitive risks), quick wins (low-effort improvements), provider strategies (per-AI-provider tactics), content recommendations (what to create), and prompt edit suggestions. Requires read:insights scope (Professional tier or above).
| Name | Type | Req | Description |
|---|---|---|---|
| category | – | – | Filter by team: "tech", "content", "marketing", or "leadership" |
| priority | – | – | Filter by priority: "high", "medium", or "low" |
| report_id | string | yes | UUID of the report to get insights for (required) |
| type | – | – | Filter by category: "opportunities", "threats", "quick_wins", "provider_strategies", "content_recommendations", or "all" |
| workspace_id | – | – | Optional. UUID of the workspace. Required only for agency/company keys with >1 workspace. |
Structured output declared, but exposes no named fields.
No examples provided.
get_metric_catalog Get Metric Catalog ~44
Discover what data is available in this Nudg3 workspace. Returns available investigation tools, filters, and export datasets. No API call is made — this is a static catalog.
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
get_overview Get Overview ~192
Compact workspace health check — start here. Returns current visibility scores for all tracked brands (primary + competitors), 7-day and 30-day trends as single numbers (not raw time-series), top cited sources, anomaly alerts, and suggested next investigations with parameters. This is the first tool to call. Follow the suggested_investigations in the response to drill deeper into specific areas.
| Name | Type | Req | Description |
|---|---|---|---|
| date_range | string | – | "7d", "30d", "90d", or "YYYY-MM-DD:YYYY-MM-DD" |
| providers | – | – | Filter by AI provider names (e.g. ["ChatGPT", "Gemini"]) |
| workspace_id | – | – | Optional. UUID of the workspace to target. Required only for agency/company-scoped keys that authorise more than one workspace — pass it from the list_workspaces response. For workspace-scoped keys (… |
Structured output declared, but exposes no named fields.
No examples provided.
get_reports Get Reports ~239
List or retrieve visibility audit reports. Reports contain point-in-time snapshots of brand visibility including scores, competitive position, executive summaries, and AI-generated insights. Three modes: - List: returns paginated report summaries (default) - Latest: set latest=True to get the most recent report - Detail: pass report_id for full report with optional insights Requires read:insights scope (Professional tier or above).
| Name | Type | Req | Description |
|---|---|---|---|
| end_date | – | – | Filter by report date (YYYY-MM-DD) |
| include_insights | boolean | – | Embed AI insights in the report detail response |
| latest | boolean | – | If true, returns the most recent report |
| page | integer | – | Page number for list mode |
| per_page | integer | – | Results per page (max 50) |
| report_id | – | – | UUID of a specific report (returns full detail) |
| report_type | – | – | Filter: "free_audit", "weekly_report", or "on_demand" |
| start_date | – | – | Filter by report date (YYYY-MM-DD) |
| workspace_id | – | – | Optional. UUID of the workspace. Required only for agency/company keys with >1 workspace. |
Structured output declared, but exposes no named fields.
No examples provided.
list_workspaces List Workspaces ~163
List the workspaces this API key can access. Two personas: - Workspace-scoped key (default for single-brand customers): returns a single-element list with the key's bound workspace. - Agency or company-scoped key (multi-client portfolio): returns every workspace nested under each company in the portfolio. Use this first when an agency user asks "which workspaces do I have?" or before drilling into a specific brand. When an agency key has more than one workspace, every other tool will ask you to specify ``workspace_id`` explicitly — the response from this tool is what you pick from. No arguments required. Returns the live response from GET /api/v1/workspaces verbatim (scope_level, agency, companies tree).
Input schema present but exposes no named parameters.
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the ai.nudg3/brand-intelligence MCP server?
ai.nudg3/brand-intelligence runs locally as a PyPI package, launched with uvx nudg3-mcp. Ready-made configuration for Claude, Cursor, VS Code, Codex and 5 more is on this page, copied from each client's own documentation.
pypi · nudg3-mcp
claude mcp add ai-nudg3-brand-intelligence -- uvx nudg3-mcp
What is the ai.nudg3/brand-intelligence MCP server?
ai.nudg3/brand-intelligence is an MCP server listed in the public MCP registry as ai.nudg3/brand-intelligence. Query your Nudg3 brand visibility across ChatGPT, Claude, Gemini, AI Overviews, and Perplexity. This page covers its PyPI package (nudg3-mcp).
Is the ai.nudg3/brand-intelligence MCP server safe to use?
ai.nudg3/brand-intelligence scores 58 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 4 November 2026. 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 ai.nudg3/brand-intelligence MCP server expose?
ai.nudg3/brand-intelligence exposes 11 tools: get_metric_catalog, list_workspaces, get_overview, analyze_competitors, analyze_prompts, and 6 more. Their descriptions and schemas cost roughly 2,206 tokens of context every time the server is loaded.
Is the ai.nudg3/brand-intelligence MCP server still maintained?
ai.nudg3/brand-intelligence is still listed as active in the MCP registry. We last reached this channel on 4 November 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 ai.nudg3/brand-intelligence MCP server under?
ai.nudg3/brand-intelligence declares the MIT licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.