Alchemyst MCP Skill

Use this skill whenever you need to store, retrieve, search, or view persistent context using the Alchemyst AI MCP server at mcp.getalchemystai.com/mcp/sse. Triggers include: requests to "remember" or "recall" information across sessions, storing documents/notes/decisions for later retrieval, searching project knowledge, or any task that involves reading from or writing to Alchemyst's context store.

Alchemyst AI MCP — Context Engine

Overview

Alchemyst AI is a persistent context layer for AI applications. It stores documents, conversations, and structured knowledge externally so they can be retrieved on demand — across sessions, tools, and environments.

The MCP server is exposed as an SSE (Server-Sent Events) endpoint:

text

https://mcp.getalchemystai.com/mcp/sse

Authentication is done via a Bearer token (your Alchemyst API key) passed as a request header.

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Prerequisites

Requirement	Detail
Alchemyst API key	Obtain from platform.getalchemystai.com
MCP-compatible client	Claude Desktop, Cursor, VS Code + MCP extension, or custom agent
Transport	SSE (https://mcp.getalchemystai.com/mcp/sse)
Auth header	Authorization: Bearer <YOUR_API_KEY>

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Claude Desktop Configuration

Add the following to your claude_desktop_config.json:

json

{
  "mcpServers": {
    "alchemyst": {
      "url": "https://mcp.getalchemystai.com/mcp/sse",
      "headers": {
        "Authorization": "Bearer YOUR_ALCHEMYST_API_KEY"
      }
    }
  }
}

Never commit your API key. Use an environment variable or secrets manager in production.

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Tools

The server exposes exactly four tools:

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alchemyst_ai_search_context — Semantic Search

Search the context store using a natural-language query. Returns documents ranked by semantic similarity.

When to use: Before answering a question that might rely on stored knowledge; retrieving prior decisions, docs, or instructions without manual lookup.

Input Schema

Field	Type	Required	Description
query	string	✅	Natural-language search query
similarity_threshold	number (0–1)	✅	Maximum similarity threshold — results at or below this score are returned
minimum_similarity_threshold	number (0–1)	✅	Floor — results below this score are excluded
scope	"internal" | "external"	❌	Search scope; defaults to "internal"
metadata	object | null	❌	Optional filter by file metadata; defaults to null

Metadata filter fields (all required if metadata object is provided):

Field	Type	Description
fileName	string	Name of file to filter by
fileSize	number	File size in bytes
fileType	string	MIME type
lastModified	string	ISO 8601 datetime string
groupName	string[]	Tag groups; defaults to ["default"]

Note: Metadata field names use camelCase in the search tool (fileName, fileSize, fileType, lastModified, groupName).

Threshold Guidance

• similarity_threshold: 0.8 + minimum_similarity_threshold: 0.5 → tight, precise results
• similarity_threshold: 0.7 + minimum_similarity_threshold: 0.4 → broader, more permissive
• Always set minimum_similarity_threshold lower than similarity_threshold

Example Call

json

{
  "query": "authentication token expiry policy",
  "similarity_threshold": 0.8,
  "minimum_similarity_threshold": 0.5,
  "scope": "internal",
  "metadata": null
}

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alchemyst_ai_add_context — Store Context

Add one or more documents to the Alchemyst context store.

When to use: Saving project requirements, architectural decisions, onboarding docs, meeting notes, code conventions, or any knowledge you want to persist and retrieve later.

Input Schema

Field	Type	Required	Description
user_id	string	✅	Unique identifier of the user submitting context
organization_id	string | null	✅	Organization ID; pass null if not applicable
documents	array	✅	Array of document objects, each with a content string field (plus optional extra string fields)
source	string	✅	Label describing where this context came from (e.g., "project.auth.decisions")
context_type	"resource" | "conversation" | "instruction"	✅	Type of context being stored
metadata	object	✅	File metadata — all four fields required
scope	"internal" | "external"	❌	Defaults to "internal"

Metadata fields (all required; note snake_case here, unlike the search tool):

Field	Type	Description
file_name	string	Name of the file or document
doc_type	string	MIME type or document type (e.g., "text/markdown")
modalities	string[]	Modalities present (e.g., ["text"], ["text", "image"])
size	number	Size in bytes

⚠️ Key naming difference: add_context uses snake_case metadata keys (file_name, doc_type, size), while search_context uses camelCase (fileName, fileType, fileSize). Match the case to the tool you're calling.

Context Types

Value	Use for
"resource"	Files, documents, reference material, code
"conversation"	Chat history, meeting transcripts, support threads
"instruction"	Persistent rules, conventions, agent instructions

Source Naming Convention

Use dot-separated hierarchical labels. This makes auditing straightforward:

text

project.auth.decisions
team.onboarding.v2
agent.instructions.sales

Example Call

json

{
  "user_id": "user_abc123",
  "organization_id": "org_xyz",
  "documents": [
    { "content": "All API routes use JWT auth with 15-minute token expiry." }
  ],
  "source": "project.auth.decisions",
  "context_type": "resource",
  "scope": "internal",
  "metadata": {
    "file_name": "auth-decisions.md",
    "doc_type": "text/markdown",
    "modalities": ["text"],
    "size": 64
  }
}

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alchemyst_ai_context_mcp_view_context — View Context Summary

Retrieve a summary of all stored context for a given user and organization.

When to use: Auditing what's in the context store, debugging missing context, or checking what knowledge is available before a session.

Input Schema

Field	Type	Required	Description
user_id	string	✅	User ID to get context for
organization_id	string | null	✅	Organization ID; pass null if not applicable

Example Call

json

{
  "user_id": "user_abc123",
  "organization_id": "org_xyz"
}

---

alchemyst_ai_context_mcp_view_docs — View Stored Documents

Retrieve the actual documents stored in the context store for a given user and organization.

When to use: Listing stored documents, verifying content was saved correctly, or browsing available knowledge before deciding what to add.

Input Schema

Field	Type	Required	Description
user_id	string	✅	User ID to get documents for
organization_id	string | null	✅	Organization ID; pass null if not applicable

Example Call

json

{
  "user_id": "user_abc123",
  "organization_id": "org_xyz"
}

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Workflow Patterns

Store → Search (basic memory pattern)

1. Call alchemyst_ai_add_context to store a document
2. Later, call alchemyst_ai_search_context with a relevant query to retrieve it
3. Inject the retrieved content into your prompt as context

Audit before adding

1. Call alchemyst_ai_context_mcp_view_docs to inspect what's already stored
2. Only call alchemyst_ai_add_context if the knowledge isn't already present
3. This avoids duplicating context and keeps the store clean

Pre-answer retrieval

Before answering any question that might depend on project-specific knowledge, call alchemyst_ai_search_context first. Prefer doing this proactively — don't wait for the user to explicitly ask "check the context store."

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Best Practices

Always populate metadata. The metadata object is required on add_context — populate all four fields every time. Missing metadata degrades retrieval quality significantly.

Chunk large documents. Break large files into logical sections before adding. Each chunk should be independently meaningful. Don't split mid-sentence or mid-concept.

Version your sources. When content evolves, use versioned source labels (project.arch.v1, project.arch.v2) rather than re-adding to the same source. This preserves history.

Search before storing. Run a search first to check whether similar content already exists before calling add_context. Avoid accumulating duplicates.

Mind the camelCase/snake_case split. The metadata schema differs between tools — this is a quirk of the current API. Double-check field names when building payloads:

• add_context → file_name, doc_type, size (snake_case)
• search_context → fileName, fileType, fileSize (camelCase)

Pass organization_id explicitly. Even when there's no org, pass null rather than omitting the field — it's required by the schema.

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Error Handling

Status	Meaning	Action
400	Bad request	Check required fields; verify documents is an array; check metadata schema
401	Auth failure	Verify API key; confirm header is Authorization: Bearer <key>
403	Permission denied	Check org/user scope permissions
404	Not found	Confirm the user_id or organization_id is valid
422	Unprocessable entity	Schema validation failed — check field types, required fields, and camelCase vs snake_case
429	Rate limit	Back off exponentially; retry after delay
500+	Server error	Retry twice with backoff; check status.getalchemystai.com

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Resources

• Docs: getalchemystai.com/docs
• MCP overview: getalchemystai.com/docs/mcps/introduction
• Claude Desktop setup: getalchemystai.com/docs/mcps/claude-desktop
• API status: status.getalchemystai.com
• Python SDK: pip install alchemystai
• Support: [e-mail]