Scout Intel MCP — The Google for AI Agents
PYPI · SCOUT-INTEL-MCP · 2 COMPONENTS
The Google for AI agents — company intel, competitor tracking, market research via MCP. JSON output
Available components
Last scanned (4 hours ago)
Scores higher than 77% of PyPI servers
Category Search and Retrieval
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 Security100
- No malware found by supply-chain analysis.Pass
- 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
- 2 of 36 dependencies flagged as unhealthy. View diagnostics → Partial
Provenance & Transparency19
- Repository check failed: no source repository is declared. 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 (AGPL-3.0).Pass
- Actively maintained (last published 193 days ago).Pass
- Disclosure check failed: no security disclosure policy was found in the source repository. See how to fix → Fail
Schema Quality & AI Usability82
- AI-judged instruction clarity (excellent).Pass
- Tool/resource definitions use about 519 tokens (~74/item across 7 items; 7 tools + 0 resources), lean.Pass
- Usage-examples check failed: none of the tools include examples. See how to fix → Fail
Stability & Change Management83
- Stability observed for 25 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 7 captured tool definition(s), and no name or description among them implies an irreversible operation.Pass
- An AI judge read all 7 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.
- 4 Nov 26 +12
- Malware scan: unverified → pass ▲ security
- Stability: pass → 0.80 functional
- 30 Oct 26 −15
- Malware scan: pass → unverified ▼ security
- 29 Oct 26 +16
- Malware scan: unverified → pass ▲ security
- Stability: 0.97 → pass security
- 28 Oct 26 −16
- Malware scan: pass → unverified ▼ security
- Stability: pass → 0.97 functional
- 22 Oct 26 +1
- Stability: 0.97 → pass security
- 21 Oct 26 −1
- Stability: pass → 0.97 functional
- 19 Oct 26 +15
- Malware scan: unverified → pass ▲ security
- 18 Oct 26 −15
- Malware scan: pass → unverified ▼ 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 5 Nov 2026 · Analysed pypi/scout-intel-mcp@1.0.3
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 36 packages
| Packages resolved | 36 |
|---|---|
| Stale | 2 |
| 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 →
scout_batch Scout Batch ~86
Run multiple scout queries in parallel. Perfect for competitive landscape analysis and bulk research. Each query is: {"tool": "company|market|competitors|trends|product", "params": {...}}
| Name | Type | Req | Description |
|---|---|---|---|
| max_parallel | integer | – | Max concurrent queries (default 5) |
| queries | array | yes | List of {"tool": str, "params": dict} objects |
Structured output declared, but exposes no named fields.
No examples provided.
scout_company Scout Company ~73
Get structured intelligence on any company. Returns: industry, funding, tech stack, competitors, recent news, key people.
| Name | Type | Req | Description |
|---|---|---|---|
| domain | – | – | Optional company domain (e.g., "stripe.com"). Auto-detected if not provided. |
| name | string | yes | Company name (e.g., "Stripe", "OpenAI") |
Structured output declared, but exposes no named fields.
No examples provided.
scout_competitors Scout Competitors ~76
Find and analyze competitors for any company or product. Returns: list of competitors with positioning, pricing, strengths, weaknesses.
| Name | Type | Req | Description |
|---|---|---|---|
| company_or_product | string | yes | Name of company or product (e.g., "Notion", "Figma") |
| max | integer | – | Maximum number of competitors to return (default 10) |
Structured output declared, but exposes no named fields.
No examples provided.
scout_market Scout Market ~76
Research any market or industry. Returns: market size, growth rate, CAGR, key players, trends, risks.
| Name | Type | Req | Description |
|---|---|---|---|
| depth | string | – | "summary" for quick overview, "detailed" for deeper analysis |
| query | string | yes | Market/industry to research (e.g., "AI SaaS", "electric vehicles") |
Structured output declared, but exposes no named fields.
No examples provided.
scout_person Scout Person ~72
Get intelligence on a public figure. PRO tier only. Returns: current role, background, social profiles, recent activity, achievements.
| Name | Type | Req | Description |
|---|---|---|---|
| company | – | – | Optional company context (e.g., "OpenAI") |
| name | string | yes | Person's name (e.g., "Sam Altman", "Jensen Huang") |
Structured output declared, but exposes no named fields.
No examples provided.
scout_product Scout Product ~52
Get intelligence on any product. Returns: category, pricing, ratings, features, alternatives, recent updates.
| Name | Type | Req | Description |
|---|---|---|---|
| name | string | yes | Product name (e.g., "Slack", "Linear", "Vercel") |
Structured output declared, but exposes no named fields.
No examples provided.
scout_trends Scout Trends ~84
Track trends and sentiment on any topic. Returns: sentiment score, trending direction, key developments, related topics.
| Name | Type | Req | Description |
|---|---|---|---|
| timeframe | string | – | Time window — "1d", "7d", "30d", "1y" (default "7d") |
| topic | string | yes | Topic to track (e.g., "generative AI", "remote work") |
Structured output declared, but exposes no named fields.
No examples provided.
How do I install the Scout Intel MCP — The Google for AI Agents server?
Scout Intel MCP — The Google for AI Agents runs locally as a PyPI package, launched with uvx scout-intel-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 · scout-intel-mcp
claude mcp add omniologynow-rgb-scout-intel-mcp -- uvx scout-intel-mcp
What is the Scout Intel MCP — The Google for AI Agents server?
Scout Intel MCP — The Google for AI Agents is listed in the public MCP registry as io.github.omniologynow-rgb/scout-intel-mcp. The Google for AI agents, company intel, competitor tracking, market research via MCP. JSON output. This page covers its PyPI package (scout-intel-mcp).
Is the Scout Intel MCP — The Google for AI Agents server safe to use?
Scout Intel MCP — The Google for AI Agents scores 78 out of 100 on VerifyMCP. We found no known CVEs affecting it as of 5 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 Scout Intel MCP — The Google for AI Agents server expose?
Scout Intel MCP — The Google for AI Agents exposes 7 tools: scout_company, scout_market, scout_competitors, scout_trends, scout_product, and 2 more. Their descriptions and schemas cost roughly 519 tokens of context every time the server is loaded.
Is the Scout Intel MCP — The Google for AI Agents server still maintained?
Scout Intel MCP — The Google for AI Agents 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.
What licence is the Scout Intel MCP — The Google for AI Agents server under?
Scout Intel MCP — The Google for AI Agents declares the AGPL-3.0 licence, which is OSI-approved. That covers the source only, and says nothing about the cost of any service it calls.