How do I use elephantasm-mcp?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@elephantasm-mcp search my knowledge for best practices in error handling" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

elephantasm-mcp

by kaminocorp

Overview Schema Related Servers Score Discussions

Python

Remote

Elephantasm

elephantasm-mcp

MCP server for long-term agent memory. Give any AI agent persistent memory, searchable knowledge, and an evolving identity.

PyPI version Python 3.10+ License

Website · Docs · PyPI · GitHub

Model Context Protocol server for Elephantasm Long-Term Agentic Memory. One line of config.

Installation

pip install elephantasm-mcp

Related MCP server: Selti

Quick Start

Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "elephantasm": {
      "command": "elephantasm-mcp",
      "env": {
        "ELEPHANTASM_API_KEY": "sk_live_...",
        "ELEPHANTASM_ANIMA_ID": "your-anima-id"
      }
    }
  }
}

Claude Code

Add to .claude/settings.json or ~/.claude/settings.json:

{
  "mcpServers": {
    "elephantasm": {
      "command": "elephantasm-mcp",
      "env": {
        "ELEPHANTASM_API_KEY": "sk_live_...",
        "ELEPHANTASM_ANIMA_ID": "your-anima-id"
      }
    }
  }
}

Cursor / Windsurf

Add to your MCP config (Settings > MCP Servers):

{
  "elephantasm": {
    "command": "elephantasm-mcp",
    "env": {
      "ELEPHANTASM_API_KEY": "sk_live_...",
      "ELEPHANTASM_ANIMA_ID": "your-anima-id"
    }
  }
}

Any MCP-Compatible Agent

The server uses stdio transport — any MCP client that can spawn a subprocess works out of the box:

ELEPHANTASM_API_KEY=sk_live_... ELEPHANTASM_ANIMA_ID=... elephantasm-mcp

Configuration

Variable	Required	Default	Description
`ELEPHANTASM_API_KEY`	Yes	—	Your API key (starts with `sk_live_`)
`ELEPHANTASM_ANIMA_ID`	No	—	Default anima ID (can be overridden per tool call)
`ELEPHANTASM_ENDPOINT`	No	`https://api.elephantasm.com/api`	API endpoint

Get your API key and anima ID from the Elephantasm dashboard.

Tools

Five tools give your agent full read/write access to its memory:

`query` — Search the brain

Cross-source semantic search across memories, knowledge, and identity in one call. Returns a pre-formatted context string ready for system prompt injection.

query("what does the user prefer for error handling")

Parameter	Type	Default	Description
`query`	string	required	Natural language search query
`anima_id`	string	env default	Override default anima
`sources`	string[]	all	Filter: `"memories"`, `"knowledge"`, `"identity"`
`max_tokens`	int	2000	Token budget for results
`limit`	int	20	Max results
`exclude_ids`	string[]	—	Exclude IDs for multi-turn dedup

`search_memories` — Find past experiences

Semantic search across structured memories (reflections on past interactions).

Parameter	Type	Default	Description
`query`	string	required	Search query
`anima_id`	string	env default	Override default anima
`limit`	int	10	Max results
`threshold`	float	0.7	Min similarity (0–1)

`search_knowledge` — Look up what the agent knows

Semantic search across canonicalized knowledge — facts, concepts, methods, principles, and experiences.

Parameter	Type	Default	Description
`query`	string	required	Search query
`anima_id`	string	env default	Override default anima
`limit`	int	10	Max results
`threshold`	float	0.7	Min similarity (0–1)
`knowledge_type`	string	—	Filter: `FACT`, `CONCEPT`, `METHOD`, `PRINCIPLE`, `EXPERIENCE`

`get_identity` — Read the agent's personality

Retrieve the agent's behavioral fingerprint: personality type, communication style, and self-reflection.

Parameter	Type	Default	Description
`anima_id`	string	env default	Override default anima

`ingest_event` — Record an interaction

Capture a message, tool call, or system event. Queued for automatic memory synthesis.

Parameter	Type	Default	Description
`content`	string	required	Event content
`event_type`	string	`message.in`	`message.in`, `message.out`, `tool.call`, `tool.result`, `system`
`anima_id`	string	env default	Override default anima
`role`	string	—	`user`, `assistant`, `system`, `tool`
`author`	string	—	Who generated this event
`session_id`	string	—	Group related events

Resources

Two resources provide context that MCP clients can embed directly into system prompts:

URI	Type	Description
`anima://{id}/pack`	`text/plain`	Latest compiled memory pack — memories, knowledge, identity, and temporal context assembled into a single prompt-ready string
`anima://{id}/identity`	`application/json`	Identity profile — personality type, communication style, self-reflection

How It Works

Elephantasm is a Long-Term Agentic Memory framework. The MCP server connects your agent to its memory backend:

Your Agent ←→ elephantasm-mcp ←→ api.elephantasm.com ←→ PostgreSQL + pgVector

Write path: ingest_event → Events accumulate → Memory Synthesis (LLM) → Knowledge Synthesis (LLM) → Identity evolution

Read path: query / search_memories / search_knowledge → pgVector semantic search → scored, formatted results

Memory is curated automatically by two background loops:

Dreamer — decays, merges, splits, and archives memories
Meditator — clusters, merges, reclassifies, and refines knowledge

SDKs

For programmatic access without MCP, use the native SDKs:

Python: elephantasm — pip install elephantasm
TypeScript: @elephantasm/client — npm install @elephantasm/client

License

Apache 2.0

Available Tools

5 tools

get_identityB

Get an Anima's identity profile — personality type, communication style, and self-reflection data.

ParametersJSON Schema

Name	Required	Description	Default
`anima_id`	No

TDQS

B3.3/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Without annotations, the description carries full burden. It implies a read operation ('Get') but does not disclose any potential side effects, authentication needs, or rate limits. The behavior is simple, so score is adequate but not enhanced.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single concise sentence that front-loads the main purpose. While it lacks detail, there is no unnecessary fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the low complexity (1 optional parameter, no output schema), the description is minimally complete. However, it fails to document the parameter or return value, leaving gaps for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The sole parameter 'anima_id' is not described in the description text. Schema coverage is 0%, so the description should explain the parameter's purpose, but it only implies it identifies the Anima. No format or examples are provided.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Get' and the resource 'Anima's identity profile', listing specific data components (personality type, communication style, self-reflection data). It distinguishes well from sibling tools like query or search_knowledge.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance is given on when to use this tool vs alternatives like search_memories or query. There is no mention of prerequisites, context, or exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

ingest_eventA

Record an interaction event for future memory synthesis. Use this to capture messages, tool calls, or other signals that should become part of the Anima's long-term memory.

ParametersJSON Schema

Name	Required	Description
`content`	Yes	Event content (message text, tool output, etc.)
`event_type`	No	Event type (default: message.in)
`anima_id`	No
`role`	No	Message role (user, assistant, etc.)
`author`	No	Author identifier
`session_id`	No	Session grouping ID

TDQS

A3.7/5.0

Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description bears full burden. It reveals the tool is a write operation for memory storage, but lacks details on idempotency, side effects, rate limits, or what happens on duplicate events.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no wasted words. The first sentence defines purpose, the second extends usage guidance. Front-loaded and efficient.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

While the description covers purpose and basic usage, it lacks details about return values or behavioral guarantees. For a logging tool with 6 parameters, some additional context (e.g., what the response looks like) would improve completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Description adds no parameter information beyond the schema. Schema coverage is high (83%), so baseline 3 is appropriate. The description does not clarify any parameters further.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'Record' and the resource 'interaction event', explaining its purpose for future memory synthesis. This distinguishes it from sibling tools like 'query' or 'search' which are read-oriented.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides clear context: 'Use this to capture messages, tool calls, or other signals' for memory synthesis. It does not explicitly exclude scenarios, but the context is sufficient given sibling tools are all reads.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

queryA

Search an Anima's brain across memories, knowledge, and identity. Returns relevant results ranked by semantic similarity to your query. Use this for general questions about what the Anima knows or remembers.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Natural language query to search the Anima's brain
`anima_id`	No	Anima ID (optional, uses default if not set)
`sources`	No	Which layers to search (default: all)
`max_tokens`	No	Token budget for results (default: 2000)
`limit`	No	Max number of results (default: 20)
`exclude_ids`	No	IDs to exclude (for multi-turn dedup)

TDQS

A4.2/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Discloses that it returns results ranked by semantic similarity. No annotations provided, so description carries burden; it adequately conveys read-only behavior through the term 'search' but lacks details on limitations or edge cases.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences with no wasted words. Front-loaded with purpose and output behavior immediately.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema, but description mentions 'returns relevant results ranked by semantic similarity,' which gives a general idea. Could be more specific about return format, but sufficient for a search tool with many parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so each parameter is already described. The tool description does not add significant meaning beyond what the schema provides, meeting baseline expectations.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clearly states it searches across memories, knowledge, and identity, returning results ranked by semantic similarity. Distinguishes from siblings like search_knowledge and search_memories by being a general cross-layer query.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this for general questions about what the Anima knows or remembers,' providing clear context. Does not explicitly mention when not to use or alternatives, but sibling tools imply specialization.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_knowledgeA

Semantic search across an Anima's knowledge base. Returns canonicalized truths (facts, concepts, methods, principles, experiences) ranked by similarity. Use this for structured knowledge retrieval.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Search query
`anima_id`	No
`limit`	No	Max results (default: 10)
`threshold`	No	Min similarity 0-1 (default: 0.7)
`knowledge_type`	No	Filter by knowledge type

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description bears full burden. It discloses the semantic search behavior and return type but omits details on read-only nature, pagination, error handling, or permission requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise with three sentences: purpose, return type, and usage guidance. It is front-loaded with the most critical information and contains no redundant text.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 5 parameters, no output schema, and no annotations, the description covers core purpose and return type but lacks details on parameter usage (e.g., anima_id, limits) and return format, leaving gaps.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description adds context about return types (facts, concepts, etc.) which complements the 'knowledge_type' parameter, but does not elaborate on other parameters beyond the schema's 80% coverage. The added value is moderate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs 'semantic search across an Anima's knowledge base' and returns 'canonicalized truths' ranked by similarity. It distinguishes from siblings like 'search_memories' by specifying structured knowledge retrieval and listing knowledge types.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description advises 'Use this for structured knowledge retrieval,' providing clear context for when to use the tool. However, it does not explicitly mention when not to use it or name alternative tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

search_memoriesA

Semantic search across an Anima's memories. Returns memories ranked by similarity to your query. Use this when you want to find specific past experiences or interactions.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Search query
`anima_id`	No
`limit`	No	Max results (default: 10)
`threshold`	No	Min similarity 0-1 (default: 0.7)

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It mentions semantic search and ranking by similarity, which is helpful, but doesn't disclose whether it's read-only, required auth, rate limits, or behavior on empty results. Adequate but not comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with purpose, no redundant words. Efficient and to the point.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Lacks output schema or description of return format. The tool is simple, but missing details about response structure (e.g., returns memory IDs and similarity scores) reduces completeness for an agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers 75% of parameters with descriptions. The description adds context (semantic search, ranking) but does not elaborate on anima_id or parameter specifics beyond schema. Marginal added value over schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it performs semantic search across memories and returns ranked results. It distinguishes itself from siblings like search_knowledge (for knowledge) and get_identity, making the purpose unambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explicitly says 'Use this when you want to find specific past experiences or interactions,' providing clear usage context. Lacks explicit exclusions or alternatives, but the sibling list and resource type imply when not to use.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

5 tool updatesv0.1.0Jun 7, 2026
- First observedget_identity
- First observedingest_event
- First observedquery
- First observedsearch_knowledge
- First observedsearch_memories

Install Server

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TDQS

A3.6/5.0

Disambiguation3/5

There is overlap between 'query', 'search_knowledge', and 'search_memories', as all three perform semantic search; descriptions help differentiate but ambiguity remains.

Naming Consistency3/5

Most tools follow a verb_noun pattern (get_identity, ingest_event, search_knowledge, search_memories), but 'query' is a single verb without a noun, breaking consistency.

Tool Count5/5

5 tools is well-scoped for the domain of managing an Anima's identity, events, and memory; each tool serves a clear purpose.

Completeness3/5

Core operations (get identity, record events, search) are present, but missing update/delete for identity and events, and no explicit tool to add knowledge directly.

Maintenance

ActivityInactive

ResponsivenessNo issues

Resources

GitHub Repository

Need Help?

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MCP directory API

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elephantasm-mcp

Installation

Quick Start

Claude Desktop

Claude Code

Cursor / Windsurf

Any MCP-Compatible Agent

Configuration

Tools

query — Search the brain

search_memories — Find past experiences

search_knowledge — Look up what the agent knows

get_identity — Read the agent's personality

ingest_event — Record an interaction

Resources

How It Works

SDKs

Links

License

Available Tools

Tool Schema Changelog

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Latest Blog Posts

MCP directory API

`query` — Search the brain

`search_memories` — Find past experiences

`search_knowledge` — Look up what the agent knows

`get_identity` — Read the agent's personality

`ingest_event` — Record an interaction