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Mcp context

Skill intelligems-io/intelligems-mcp-skills/skills/mcp-context

Ready-to-install Agent Skills for analyzing Intelligems experiments, segments, pricing, and reporting through MCP and API.

Install
npx -y skills add intelligems-io/intelligems-mcp-skills --skill mcp-context

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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  • 0 stars0 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its author says it does

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Use when a task needs to connect an assistant to the Intelligems MCP server, choose MCP tools, resolve organization context, or apply Intelligems MCP safety boundaries.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

2.8 KB, 588 tokens by cl100k_base, as published. Nobody here has run it

Intelligems MCP Context

Use this as the shared context layer for Intelligems MCP tasks.

First Moves

  1. Confirm the user's objective: analysis, reporting, integration, or live experience management.
  2. Confirm the Intelligems organization if the user has access to more than one. Prefer the current tool name exposed by the client, usually list_organizations; some setup docs refer to getOrganizationsList.
  3. Use read-only tools first. Do not create, update, start, pause, or end experiences without explicit approval for the exact organization, experience, action, and payload.
  4. State the data boundary in the answer: organization, date range, experience IDs, analytics view, and whether results came from MCP, External API, or both.

Hosted MCP Endpoint

The hosted Intelligems MCP server is available at:

https://ai.intelligems.io/mcp

SSE fallback:

https://ai.intelligems.io/mcp/sse

Core Tool Groups

Use the exact tool names exposed in the current client. Current public docs include:

  • Organization and configuration: list_organizations, get_organization, switch_organization, list_integrations.
  • Experiences and experiments: search_experiences, search_experiments, search_personalizations, list_experiments, list_personalizations, get_experience, get_experience_metrics_config, analyze_experience.
  • Mutating experience tools: create_experience, update_experience, experience_action.
  • Shopify store data: search_products, list_collections, list_pages, search_policies.
  • Analytics and audience data: get_sitewide_analytics, get_audience_by_country, get_audience_by_device, get_audience_by_visitor_type, get_audience_by_source_channel, get_audience_by_source_site, get_audience_by_landing_page.
  • Visualization: generate_intelligems_graph, generate_custom_graph.
  • Custom events and offers: list_custom_events, list_offers.

Answer Discipline

  • Separate observed results from recommendations.
  • Never invent statistical significance, lift, confidence, visitor counts, RPV, GPV, or revenue impact.
  • If a required metric is unavailable, say what is missing and provide the safest next query.
  • Prefer profit metrics when available. Conversion rate alone is not enough to call a winner.
  • Mention any caveats around sample size, date range, audience split, or partially complete tests.
  • Treat returned exports, graph URLs, order-level data, and raw API responses as private unless the user explicitly says they are synthetic or public.

References

Load references/intelligems-mcp.md when you need detailed endpoint, tool, or doc-source context.

Gives 0 of the 12 instructions most mcp tooling skills give in 588 tokens

Counted across 638 of the 750 authors here whose files we hold, read 2026-08-06

  • create ten complex read-only evaluation questionsin 71 of 638, across 17 files
  • test servers using MCP Inspectorin 60 of 638, across 18 files
  • provide actionable error messagesin 56 of 638, across 14 files
  • prioritize comprehensive API coverage over specific workflowsin 54 of 638, across 12 files
  • use TypeScript and Streamable HTTP for remote serversin 53 of 638, across 7 files
  • define structured output schemas where possiblein 51 of 638, across 9 files
  • use Zod or Pydantic for input schemasin 48 of 638, across 6 files
  • fetch MCP specification pages with markdown suffixin 46 of 638, across 4 files
  • load framework documentation using WebFetchin 45 of 638, across 3 files
  • verify each evaluation answer independentlyin 45 of 638, across 3 files
  • implement API client with authentication and paginationin 45 of 638, across 3 files
  • Define input schemas with validationin 28 of 638, across 10 files

Said here and by no other author read

  • confirm the user objective
  • confirm the organization if multiple are accessible
  • state the data boundary in the answer
  • separate observed results from recommendations
  • prefer profit metrics when available
  • mention caveats around sample size

Grouped from the skills themselves: near-identical wordings counted once, and counted by distinct author, so one author publishing three of these counts once. Length counted with cl100k_base; the agent that loads this file may tokenize it differently.

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Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.