# io.github.Dakera-AI/dakera-mcp (oci · ghcr.io/dakera-ai/dakera-mcp:0.10.12)

Agent memory engine — 86 MCP tools, self-hosted, single Rust binary

- Trust score: 43/100 (low)
- Change this week: +4
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-09-20

## Components

- oci · `ghcr.io/dakera-ai/dakera-mcp:0.10.12`: 43/100 (this document), [markdown](https://verifymcp.io/servers/dakera-ai-dakera-mcp/ghcr-io-dakera-ai-dakera-mcp-0-10-12.md), [page](https://verifymcp.io/servers/dakera-ai-dakera-mcp/ghcr-io-dakera-ai-dakera-mcp-0-10-12)

## Channel facts

- Registry: `oci`
- Package: `ghcr.io/dakera-ai/dakera-mcp:0.10.12`
- Transport: `stdio`

## Trust breakdown

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. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-09-20.

- **Supply Chain Security**: 0/100
  - Malware scan not yet available for this package.
  - Known CVEs could not be checked: this artifact ships no SBOM, so there is no dependency list to read. Publishing one would let us assess it.
  - Install-script risk not yet assessed.
  - Dependency health could not be checked: this artifact ships no SBOM, so there is no dependency list to read. Publishing one would let us assess it.
- **Provenance & Transparency**: 35/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - License check failed: no license is declared.
  - Actively maintained (last published 17 days ago).
  - Publishes a security disclosure policy (SECURITY.md).
- **Schema Quality & AI Usability**: 75/100
  - AI-judged instruction clarity (good).
  - Context-footprint check failed: tool/resource definitions use about 1581 tokens (~112/item across 14 items; 14 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 57/100
  - Stability observed for 17 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 92/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 77% of tool parameters carry a description.
- **Tool Safety**: 100/100
  - No prompt-injection markers were found in the server instructions, tool names or descriptions we captured.
  - We read all 14 captured tool definition(s), and no name or description among them implies an irreversible operation.
  - An AI judge read all 14 captured unit(s) of tool text and found none that tries to manipulate the model reading it.
- **Capabilities**: 20/100
  - Spec-recency check failed: implements MCP spec 2024-11-05; the latest is 2026-07-28.

**Unverified: 1 category.** A category scored 0 because we could not verify it: a data source with nothing on this package, evidence we could not reach, or a check we could not run. We only credit what we can confirm.

## Install

### How do I install the io.github.Dakera-AI/dakera-mcp server?

io.github.Dakera-AI/dakera-mcp runs locally as a container image, launched with docker run --rm -i ghcr.io/dakera-ai/dakera-mcp:0.10.12. Ready-made configuration for Claude, Cursor, VS Code, Codex and 3 more is on this page, copied from each client's own documentation.

### Claude

```bash
claude mcp add dakera-ai-dakera-mcp -- docker run --rm -i ghcr.io/dakera-ai/dakera-mcp:0.10.12
```

### Cursor

```json
{
  "mcpServers": {
    "dakera-ai-dakera-mcp": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "ghcr.io/dakera-ai/dakera-mcp:0.10.12"
      ]
    }
  }
}
```

### VS Code

```json
{
  "servers": {
    "dakera-ai-dakera-mcp": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "ghcr.io/dakera-ai/dakera-mcp:0.10.12"
      ]
    }
  }
}
```

### Codex

```bash
codex mcp add dakera-ai-dakera-mcp -- docker run --rm -i ghcr.io/dakera-ai/dakera-mcp:0.10.12
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "dakera-ai-dakera-mcp": {
      "type": "local",
      "command": [
        "docker",
        "run",
        "--rm",
        "-i",
        "ghcr.io/dakera-ai/dakera-mcp:0.10.12"
      ],
      "enabled": true
    }
  }
}
```

### Hermes

```yaml
mcp_servers:
  dakera-ai-dakera-mcp:
    command: "docker"
    args: ["run", "--rm", "-i", "ghcr.io/dakera-ai/dakera-mcp:0.10.12"]
```

### Netclaw

```json
{
  "McpServers": {
    "dakera-ai-dakera-mcp": {
      "Transport": "stdio",
      "Command": "docker",
      "Arguments": [
        "run",
        "--rm",
        "-i",
        "ghcr.io/dakera-ai/dakera-mcp:0.10.12"
      ]
    }
  }
}
```

### Other

```json
{
  "mcpServers": {
    "dakera-ai-dakera-mcp": {
      "command": "docker",
      "args": [
        "run",
        "--rm",
        "-i",
        "ghcr.io/dakera-ai/dakera-mcp:0.10.12"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-09-20 (score 43, +1)

No change was recorded against any check on this day. Stability & Change Management went from 53 to 57. That category is still filling its 30-day observation window: 16 days of observed history at the previous scan, 17 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-18 (score 42, +1)

No change was recorded against any check on this day. Stability & Change Management went from 47 to 50. That category is still filling its 30-day observation window: 14 days of observed history at the previous scan, 15 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-16 (score 41, +1)

No change was recorded against any check on this day. Stability & Change Management went from 40 to 43. That category is still filling its 30-day observation window: 12 days of observed history at the previous scan, 13 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-14 (score 40, +1)

No change was recorded against any check on this day. Stability & Change Management went from 33 to 37. That category is still filling its 30-day observation window: 10 days of observed history at the previous scan, 11 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-12 (score 39, +1)

No change was recorded against any check on this day. Stability & Change Management went from 27 to 30. That category is still filling its 30-day observation window: 8 days of observed history at the previous scan, 9 at this one. The score rises as the window fills, whether or not the server changes.

### 2026-09-11 (score 38, +3)

- [functional improvement] Stability: unverified → 0.27

### 2026-09-03 (score 35)

First indexed and scored.

## MCP tools (14)

### `dakera_store` (~152 tokens)

Persist a new memory for an agent with importance weighting and optional tags. Use to save facts, decisions, or context for future retrieval. importance defaults to 0.5; set 0.8–1.0 for critical memories that must survive decay.

Input parameters:

- `agent_id` (string, required)
- `content` (string, required): Memory content text
- `expires_at` (integer): Expiry Unix timestamp (seconds)
- `importance` (number): Importance 0.0-1.0
- `memory_type` (string): Memory type (episodic|semantic|procedural|working)
- `session_id` (string): Session to associate with
- `tags` (array): Tags for filtering

### `dakera_recall` (~145 tokens)

Retrieve top-k memories semantically closest to a query. Prefer over dakera_batch_recall for query-based retrieval. Set include_associated=true to expand results via KG edges (1-3 hops).

Input parameters:

- `agent_id` (string, required)
- `include_associated` (boolean): Include KG-linked memories in results
- `min_importance` (number): Min importance threshold
- `query` (string, required): Semantic query text
- `since` (string): Only memories created at or after this ISO-8601 timestamp
- `top_k` (integer): Max results to return
- `until` (string): Only memories created at or before this ISO-8601 timestamp

### `dakera_forget` (~87 tokens)

Permanently delete memories by ID or tag. Provide memory_ids for exact removal or tags to bulk-delete all memories sharing those tags. Deletion is immediate and irreversible — prefer dakera_memory_importance to suppress without deleting.

Input parameters:

- `agent_id` (string, required)
- `memory_ids` (array): Specific memory IDs to delete
- `tags` (array): Delete memories with these tags

### `dakera_batch_recall` (~133 tokens)

Filter-based memory listing by tags, importance range, time window, type, or session. Prefer over dakera_recall when semantic search is not needed. At least one filter required.

Input parameters:

- `agent_id` (string, required)
- `created_after` (integer): After Unix timestamp
- `created_before` (integer): Before Unix timestamp
- `max_importance` (number): Max importance (inclusive)
- `memory_type` (string)
- `min_importance` (number): Min importance (inclusive)
- `session_id` (string)
- `tags` (array): Tags to match (all required)

### `dakera_batch_forget` (~145 tokens)

Bulk-delete memories matching filter criteria: tags, importance range, time window, or memory type. At least one filter is required to prevent accidental full-agent wipe. Deletion is permanent — use dakera_memory_importance to lower importance scores instead of deleting.

Input parameters:

- `agent_id` (string, required)
- `created_after` (integer): After Unix timestamp
- `created_before` (integer): Before Unix timestamp
- `max_importance` (number): Max importance threshold
- `memory_type` (string)
- `min_importance` (number): Min importance threshold
- `session_id` (string)
- `tags` (array): Tags to match (all required)

### `dakera_search` (~92 tokens)

Semantic search with optional tag and memory-type pre-filters. Prefer over dakera_recall when results must be constrained by tag or type alongside the semantic match.

Input parameters:

- `agent_id` (string, required)
- `memory_type` (string): Filter by memory type
- `query` (string, required): Search query text
- `tags` (array): Filter by tags
- `top_k` (integer): Number of results

### `dakera_session_start` (~59 tokens)

Open a new session, returning a session_id that groups stored memories under a shared context. Attach metadata such as task type or trigger source for later retrieval.

Input parameters:

- `agent_id` (string, required)
- `metadata` (object): Optional session metadata

### `dakera_session_end` (~67 tokens)

Close an active session with an optional summary. Always call at run end (even on error) to avoid orphaned sessions; summary is retrievable via dakera_session_get.

Input parameters:

- `session_id` (string, required): Session ID to end
- `summary` (string): Optional session summary

### `dakera_knowledge_graph` (~105 tokens)

Build a knowledge graph from a seed memory using embedding similarity. Use to explore how a concept connects to stored knowledge. For BFS traversal of an existing linked graph use dakera_graph_traverse.

Input parameters:

- `agent_id` (string, required)
- `depth` (integer): Graph traversal depth (controls candidate count)
- `memory_id` (string, required): Seed memory ID to build graph from
- `min_similarity` (number): Minimum similarity threshold 0.0-1.0

### `dakera_fulltext_search` (~92 tokens)

BM25 keyword search over indexed documents. Use over vector search when exact-term recall matters (error codes, IDs, names). For semantic+keyword combined use dakera_hybrid_search.

Input parameters:

- `filter` (object): Optional metadata filter
- `namespace` (string, required): Namespace to search in
- `query` (string, required): Search query text
- `top_k` (integer): Number of results to return

### `dakera_hybrid_search` (~175 tokens)

BM25 + vector ANN hybrid search in a single pass. Omit vector for BM25-only mode. Use for RAG when pure semantic or keyword search alone is insufficient. vector_weight: 0.0=BM25, 1.0=vector (default 0.5).

Input parameters:

- `filter` (object): Optional metadata filter
- `include_metadata` (boolean)
- `include_vectors` (boolean)
- `namespace` (string, required): Namespace to search in
- `text` (string, required): Text query for full-text search
- `top_k` (integer): Number of results to return
- `vector` (array): Query embedding; omit for BM25-only.
- `vector_weight` (number): Vector score weight 0.0–1.0; text weight = 1−value.

### `dakera_extract` (~181 tokens)

Extract structured information (entities, topics, key phrases, summary) from arbitrary text using the configured provider hierarchy: per-request override → namespace default → server default → GLiNER local. Supported providers: `gliner` (zero-config local ONNX), `openai`, `anthropic`, `openrouter`, `ollama`, `none`.

Input parameters:

- `entity_types` (array): GLiNER entity type labels (e.g. ["person", "org", "location"]). Only used when provider is `gliner`.
- `extractor_override` (object): Per-request provider override — highest priority in the resolution hierarchy. Fields: provider, model, base_url, api_key.
- `namespace` (string): Namespace whose default extractor config is used. If omitted, the server-level default is used.
- `text` (string, required): Text to extract information from

### `dakera_discover_tools` (~90 tokens)

Search the Dakera tool catalog by keyword or tier (core/power/admin/meta) and return names and one-line summaries without loading full schemas. Call this first to find relevant tools, then use dakera_load_tools to fetch only the schemas you need — avoids loading the full catalog upfront.

Input parameters:

- `query` (string): Keyword to search tool names/descriptions.
- `tier` (string)

### `dakera_load_tools` (~58 tokens)

Fetch the full inputSchema for one or more named tools. Use after dakera_discover_tools. Returns schemas for found tools and a not_found list for unrecognized names.

Input parameters:

- `tools` (array, required): Tool names to load schemas for

## Diagnostics

Captured diagnostic sections: Provenance. The full working is on the page: https://verifymcp.io/servers/dakera-ai-dakera-mcp/ghcr-io-dakera-ai-dakera-mcp-0-10-12#diagnostics

## Score history

- 2026-09-20: 43
- 2026-09-19: 42
- 2026-09-18: 42
- 2026-09-17: 41
- 2026-09-16: 41
- 2026-09-15: 40
- 2026-09-14: 40
- 2026-09-13: 39
- 2026-09-12: 39
- 2026-09-11: 38
- 2026-09-10: 35
- 2026-09-09: 35
- 2026-09-08: 35
- 2026-09-07: 35
- 2026-09-06: 35
- 2026-09-05: 35
- 2026-09-04: 35
- 2026-09-03: 35

## Common questions

### What is the io.github.Dakera-AI/dakera-mcp server?

io.github.Dakera-AI/dakera-mcp is listed in the public MCP registry as io.github.Dakera-AI/dakera-mcp. Agent memory engine, 86 MCP tools, self-hosted, single Rust binary. This page covers its container image (ghcr.io/dakera-ai/dakera-mcp:0.10.12).

### Is the io.github.Dakera-AI/dakera-mcp server safe to use?

io.github.Dakera-AI/dakera-mcp scores 43 out of 100 on VerifyMCP. 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 io.github.Dakera-AI/dakera-mcp server expose?

io.github.Dakera-AI/dakera-mcp exposes 14 tools: dakera_store, dakera_recall, dakera_forget, dakera_batch_recall, dakera_batch_forget, and 9 more. Their descriptions and schemas cost roughly 1,581 tokens of context every time the server is loaded.

### Is the io.github.Dakera-AI/dakera-mcp server still maintained?

io.github.Dakera-AI/dakera-mcp is still listed as active in the MCP registry. We last reached this channel on 20 September 2026. Those dates come from our own scans of the registry and the channel itself, not from anything the publisher announced.

## Links

- Repository: https://github.com/Dakera-AI/dakera-mcp
- Changelog RSS feed: https://verifymcp.io/servers/dakera-ai-dakera-mcp/ghcr-io-dakera-ai-dakera-mcp-0-10-12.xml
- Changelog JSON feed: https://verifymcp.io/servers/dakera-ai-dakera-mcp/ghcr-io-dakera-ai-dakera-mcp-0-10-12.json
- HTML version of this page: https://verifymcp.io/servers/dakera-ai-dakera-mcp/ghcr-io-dakera-ai-dakera-mcp-0-10-12