Mnemos is a persistent memory engine for AI coding agents that stores, retrieves, and manages project knowledge across sessions using SQLite with full-text and semantic search.
Core MCP Tools:
mnemos_store: Save memories with optional metadata — type (short_term, long_term, episodic, semantic, working), tags, category, summary, source, and project scope. Automatically deduplicates and indexes content.
mnemos_search: Hybrid FTS5 + semantic search with configurable mode (text, semantic, or hybrid) and RRF ranking, optionally scoped to a project.
mnemos_context: Assemble the most relevant memories for a query within a token budget — ideal for context injection at session start.
mnemos_get: Fetch a specific memory by ID.
mnemos_update: Modify an existing memory's content, summary, or tags (PATCH semantics).
mnemos_delete: Soft-delete a memory by ID (recoverable).
mnemos_relate: Create typed relationships between memories with optional strength, building a knowledge graph.
mnemos_maintain: Run decay scoring, archival, and garbage collection to keep the memory store efficient.
Deployment & Integration:
Runs as an MCP server (stdio) or optional REST API server
One-command autopilot setup for Claude Code, Kiro, Cursor, and Windsurf
Single Go binary with embedded SQLite — no cloud, no runtime dependencies
Optional semantic search via Ollama or OpenAI embeddings
Optional Markdown mirror for human-readable memory export
Sub-60ms operations regardless of dataset size
The autopilot knowledge base for your coding agent.
Install once. From then on, your agent builds itself a structured knowledge base of your project — while you code. No prompts to remember, no remember() calls, no API to learn.
Single Go binary. Embedded SQLite. Zero cloud. No Docker. No Python. No Node runtime.
Agent (Claude Code / Cursor / Kiro / Gemini CLI / Codex / ...)
↓ MCP stdio
mnemos serve
↓
Auto-compiled knowledge base (~/.mnemos/mnemos.db)Every memory server stores text. Mnemos compiles a knowledge base.
While other servers expect you (or a carefully-tuned prompt) to decide when to store and when to retrieve, mnemos runs a full pipeline in the background:
Agent action → mnemos auto-pipeline:
├── Quality gate (reject/rewrite low-value content)
├── 3-tier dedup (hash → fuzzy → semantic)
├── Auto-summarize (extractive, fast; LLM if available)
├── File linking (extract identifiers, link to code)
├── Type classification (episodic / long_term / semantic / skill)
├── Quality scoring (for retrieval ranking)
└── Decay scheduling (so knowledge base stays relevant)
Retrieval:
├── Hybrid search (FTS5 + optional semantic + RRF)
├── File-overlap boost (memories about active files rank higher)
├── MMR diversity (kill redundant results)
├── Adaptive packing (full content or summary based on budget)
└── Token-budget cap (always fits in context)The pipeline runs behind the MCP and hook interfaces. Agents use normal memory tools; they do not need to orchestrate deduplication, summarization, ranking, packing, or lifecycle maintenance themselves.
Related MCP server: Gingugu
Layer 1 — MCP transport. Standard MCP server, stdio, works with any MCP client.
Layer 2 — Autopilot hooks. One command (mnemos setup claude) wires hooks + steering + MCP config. Session start auto-injects relevant context. Prompt submit auto-searches on topic change. Session end verifies coverage.
Layer 3 — Auto-compiled knowledge base. Quality gate, 3-tier dedup, auto-summarization, file linking, MMR context assembly — all automatic. You never trigger them. Includes passive background daemon that continuously detects staleness, contradiction, and missing relations across your memory base.
Mem0 | Zep/Graphiti | engram | OMEGA | mnemos | |
MCP-native | ✓ | ✓ | ✓ | ✓ | ✓ |
Single binary, no runtime deps | — | — | ✓ | — | ✓ |
Zero cloud / local-first | partial | — | ✓ | ✓ | ✓ |
1-command autopilot setup | — | — | — | — | ✓ |
Auto-quality gate | — | — | — | — | ✓ |
Auto-summarization | — | — | — | — | ✓ |
Auto file-linking (git-aware) | — | — | — | — | ✓ |
MMR context assembly | — | — | — | — | ✓ |
Passive background daemon | — | — | — | — | ✓ |
Temporal knowledge graph | — | ✓ | — | — | partial (decay + supersede) |
Self-host cost | $0-cloud | ~$50/mo (Neo4j) | $0 | $0 | $0 |
Mnemos isn't trying to be Zep — different bet. Zep is the best answer if you need temporal reasoning over business facts and have enterprise infrastructure. Mnemos is the best answer if you're a coding agent user who wants an autopilot knowledge base that runs itself on your laptop.
# Homebrew (macOS / Linux)
brew install s60yucca/tap/mnemos && mnemos setup claude
# curl (verify mnemos.dev is live before using)
curl -fsSL https://mnemos.dev/install.sh | bash && mnemos setup claude
# npm wrapper
npx -y @s60yucca/mnemos setup claude
# Build from source (requires Go 1.23+)
git clone https://github.com/s60yucca/mnemos
cd mnemos && make buildSwap claude for cursor, kiro, gemini-cli, codex, or trae. Restart your client. Autopilot runs from here.
mnemos setup <client> writes:
Steering file (CLAUDE.md, .cursorrules, .kiro/steering/mnemos.md) — tells the agent what's worth storing
Hook config (.claude/hooks.json or equivalent) — wires lifecycle events
MCP config (.mcp.json) — registers mnemos serve as tool provider
Three hooks run automatically:
Session start → mnemos hook session-start
Assembles relevant memories within a token budget (MMR-diversified, file-boosted). Injects into context. Cold start < 200 ms.
Prompt submit → mnemos hook prompt-submit
Detects topic + intent changes. Auto-searches knowledge base when the shift is meaningful. Respects cooldown to avoid noise.
Session end → mnemos hook session-end
Verifies whether durable memory was captured. Optionally stores a minimal breadcrumb. Cleans up session state.
Steering tells the agent what is worth remembering. Hooks handle retrieval, dedup, summarization, linking — so the agent doesn't waste tokens thinking about memory logistics.
After upgrading Mnemos, verify both the CLI and the live MCP server:
mnemos version
mnemos doctor allThen ask your agent to call mnemos_runtime. That reports the actual MCP server process handling tool calls, including version, host, pid, started_at, executable, data_dir, and project_id. If it still shows an old version, close the MCP client and reopen it. Use pkill -f "mnemos serve" only after closing active MCP clients, or when you intentionally accept reconnecting all active Mnemos transports.
You can validate the returned JSON with:
mnemos doctor runtime --from-json runtime.json
mnemos check --mcp-runtime runtime.jsonDaily mnemos check does not require MCP runtime JSON. Public-launch or upgrade verification should pass --mcp-runtime so stale MCP servers are caught before release.
Beyond hooks, mnemos runs a background daemon that continuously improves your knowledge base:
Staleness detection — flags memories that reference deleted files or outdated patterns
Contradiction detection — finds memories that conflict with each other
Relation inference — automatically links related memories
Backfill — retroactively generates summaries for memories that lack them
Auto-compile — compiles eligible source memories into reusable project knowledge
mnemos autopilot status # check daemon state
mnemos autopilot run # trigger immediate run
mnemos autopilot run --dry-run # preview findings without writing
mnemos autopilot report # view latest findingsMnemos exposes separate views for configuration, raw activity, knowledge quality, and end-to-end loop readiness:
mnemos status # effective data path and automatic feature settings
mnemos health # raw feature firing rates and denominators
mnemos eval --project myapp # memory quality, duplication, freshness, usefulness
mnemos check --project myapp # consolidated read-only loop verification
mnemos check --launch # stricter public-launch readiness gatesmnemos check opens the database read-only. It does not run migrations,
start workers, or modify memories. mnemos check --fix is intentionally
narrow: it only archives older generated autopilot reports after producing a
verified cleanup plan.
Operation | 350 memories | 1,500 memories |
| 57 ms | 24 ms |
| 55 ms | 22 ms |
| 42 ms | 39 ms |
| 27 ms | 108 ms |
hook session-start (cold) | < 200 ms | — |
binary size | ~12 MB | — |
Hardware: M1 Pro, 16GB RAM, SQLite on SSD. Your latency may vary.
Most operations stay under 60 ms regardless of dataset size. Hook subcommands use InitLight mode — no background workers, no session interrupt.
Value benchmark (token savings, precision, gotcha avoidance) is in progress. See DOGFOODING_RUNBOOK.md for methodology. Real numbers will replace this placeholder before public launch.
Tool | What it does |
| Store a memory (full auto-pipeline runs transparently) |
| Hybrid FTS + semantic + file-overlap search |
| Assemble budget-aware, MMR-diversified context |
| Fetch by ID |
| Update content, summary, or tags |
| Soft-delete (recoverable via maintain) |
| Link two memories (supersedes, caused_by, depends_on) |
| Run decay, archival, GC, stale detection |
| Report live MCP server version, host, pid, executable, uptime, data dir, and project scope |
# Agents call these automatically via MCP. You can also use directly:
mnemos store "JWT uses RS256, 1h expiry, config in auth/config.go"
mnemos search "token expiry"
mnemos stats
mnemos maintainMost users never touch this. But if you want:
# .mnemos/config.yaml (project-local) or ~/.mnemos/config.yaml (global)
embeddings:
provider: noop # noop (default) | ollama | openai
# Pure FTS works fine. Enable semantic for meaning-based search.
quality_gate:
min_words: 5
max_words: 200
min_density: 0.3
require_specific: true # long_term memories need project identifiers
duplicate_threshold: 0.8
summarization:
extractive: true # always on, fast, offline
file_linking:
enabled: true # auto-disables outside git
hook:
enabled: true
search_cooldown: 5m
session_start_max_tokens: 2000
mmr_lambda: 0.7 # 0=max diversity, 1=max relevance
file_boost: 0.3
autopilot:
enabled: true
interval: 15m
contradiction_enabled: false
auto_compile_enabled: true
min_auto_compile_sources: 5Resolution order is: explicit --config, project-local
.mnemos/config.yaml, then ~/.mnemos/config.yaml. Environment variables use
the MNEMOS_ prefix.
Mnemos auto-classifies. Override manually via --type flag.
Type | Decay rate | Use for |
| fast (~1 day) | todos, temp notes, WIP |
| medium (~1 month) | session events, bug fixes |
| slow (~6 months) | architecture decisions |
| very slow | facts, definitions, knowledge |
| slow | repeatable procedures and operational workflows |
| managed | auto-compiled project knowledge |
I got tired of re-explaining my own project to Claude Code every morning.
I tried the existing memory servers. Most of them stored text fine. But every one expected me — or a carefully-tuned prompt — to decide when to store and when to retrieve. That's not a knowledge base. That's a database with an MCP wrapper.
Mnemos is what I built to make it actually automatic. mnemos setup claude, restart the editor, and the knowledge base compiles itself.
mnemos setup claude # writes CLAUDE.md, .claude/hooks.json, .mcp.json
mnemos setup cursor # writes .cursorrules, .mcp.json
mnemos setup kiro # writes .kiro/steering/mnemos.md, .kiro/mcp.json
mnemos setup gemini-cli # writes GEMINI.md, .gemini/settings.json, .mcp.json
mnemos setup codex # writes ~/.codex/config.toml (global, CLI + VSCode)Flags: --global (install for all projects), --force (overwrite existing), --project <id> (override project ID).
For Claude Code, --project <id> writes MNEMOS_PROJECT_ID into the MCP entry and hook commands. This is the canonical project scope used by hooks and mnemos serve; mnemos --project <id> ... is bridged to the same runtime environment for manual commands.
Claude subagents may not receive session-start hook context. The main agent should call mnemos_context before delegation and include relevant memories or memory IDs in the subagent task prompt.
Codex note: Codex uses a single global config shared between the CLI and VSCode extension.
--globaland--localare ignored. The setup command writesMNEMOS_PROJECT_IDand accepts--project <id>as an explicit override. Restart both Codex CLI and the VSCode extension after setup.
If you are using emerging AI frameworks like Trae (Solo Agent), OpenClaw (Claw bot), Paperclip, or Claude Desktop, you can easily connect Mnemos manually. Mnemos speaks standard MCP over stdio.
Just add this JSON snippet to your client's MCP configuration file (e.g., trae.json, openclaw.json, paperclip.config.json, or claude_desktop_config.json):
{
"mcpServers": {
"mnemos": {
"command": "mnemos",
"args": ["serve"]
}
}
}mnemos init # first-time setup
mnemos store "..." # store (auto-pipeline)
mnemos search "auth" # hybrid search
mnemos list --project myapp # list memories
mnemos get <id> # fetch by id
mnemos update <id> --content "..." # update
mnemos delete <id> # soft delete
mnemos relate <src> <tgt> --type supersedes # typed relation
mnemos stats # storage + quality stats
mnemos maintain # decay + stale + GC
mnemos status # automatic feature configuration
mnemos eval [--project myapp] # knowledge quality metrics
mnemos health [--project myapp] # raw feature activity
mnemos check [--project myapp] [--mcp-runtime runtime.json] # verify the automatic knowledge loop
mnemos doctor runtime --from-json runtime.json # validate live MCP server identity
mnemos check --launch # apply public-launch readiness gates
mnemos check --fix # archive older generated reports safely
mnemos serve # MCP server (stdio)
mnemos version
# Autopilot setup
mnemos setup claude | cursor | kiro | gemini-cli | codex | trae [--global] [--force] [--project <id>]
# Passive autopilot daemon
mnemos autopilot status
mnemos autopilot run [--dry-run] [--project <id>]
mnemos autopilot report [--project <id>]
# Backfill
mnemos backfill summaries --project <id> [--dry-run] [--limit N]
# Benchmark readiness
mnemos bench status
mnemos bench mode on | off
mnemos bench export [--project <id>] [--include-mixed] [--output sessions.csv]
# Hook subcommands (called by clients, not manually)
mnemos hook session-start
mnemos hook prompt-submit
mnemos hook session-endNot a chatbot memory SaaS. For user-preference recall in customer support bots, use Mem0.
Not a temporal knowledge graph database. For valid-at/invalid-at reasoning over business facts, use Zep.
Not a cloud product. There is no mnemos cloud. There will never be one.
Not framework-specific. MCP-native. Works with anything that speaks MCP.
Mnemos does one thing: give agents a knowledge base that compiles itself.
See ROADMAP.md. Short version:
v1.1.14 (current): full auto-pipeline, auto-compile, passive auto-inject, loop verification, provenance-backed benchmarks, Codex support
v1.2 (next): distribution, public benchmark evidence, MCP Registry, HN launch
v1.3 (planned): team memory via git — shared .mnemos/shared/ for teammate knowledge
v2.0+ (TBD): cross-project memory scopes, memory compaction, driven by user feedback
MIT
Distill knowledge into a compiled article
| Name | Required | Description | Default |
|---|---|---|---|
| topic | Yes | Title/subject of the compiled article | |
| content | Yes | The compiled text | |
| project_id | No | Project scope | |
| source_ids | No | Comma-separated source memory IDs |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructive behavior, but the description does not explain what gets destroyed or any side effects. It adds no behavioral context beyond the annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise phrase with no wasted words. However, it may be too brief to be fully informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 4 parameters, no output schema, and annotations indicating destruction, the description is too minimal. It fails to explain how parameters affect the compilation, what the return value is, or side effects.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already defines parameters. The description does not add any additional meaning or usage hints beyond the field names and types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The verb 'distill' is specific and the resource 'knowledge into a compiled article' is clear, but it does not differentiate from siblings like mnemos_store or mnemos_update, leaving ambiguity about what 'compile' entails compared to other tools.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, nor does it mention prerequisites or when not to use it. Siblings are listed but not compared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Assemble relevant context for a query within token budget
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Context query | |
| max_tokens | No | Token budget (default 4000) | |
| project_id | No | Project scope | |
| include_relations | No | Include related memories |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description claims 'assemble context', implying a benign read operation, but annotations indicate destructiveHint=true. The description fails to disclose that this tool may alter or delete state, creating a misleading impression.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence that front-loads the primary action. It is appropriately sized for a simple tool, though it could include a bit more context without becoming verbose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With no output schema and 4 parameters, the description is too sparse. It does not explain what 'context' means, how relations are included, or what the return format is. The behavior around token budget and destructive side effects is not elaborated.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so parameters are already well-documented. The description adds no new semantic information beyond echoing 'query' and 'token budget'. Baseline score of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses the verb 'assemble' and resource 'relevant context', which clearly indicates the tool's purpose is to compile context for a query. It distinguishes from siblings like mnemos_search (which likely searches for specific items) and mnemos_store (which saves), but does not explicitly contrast them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives like mnemos_search or mnemos_get. The description does not mention prerequisites or conditions under which this tool is preferred.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Soft-delete a memory
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The term 'soft-delete' adds value beyond annotations by indicating the operation marks data as deleted without immediate removal. However, no further behavioral details (e.g., reversibility, permission requirements, or state changes) are disclosed.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single phrase with no wasted words. It is front-loaded and efficient for its length.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a one-parameter tool with a soft-delete behavior and no output schema, the description is adequate but lacks depth—e.g., it does not clarify how a soft-deleted memory can be recovered or whether this affects search results.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so 'id' is already documented. The description adds no additional meaning beyond what the schema provides, earning a baseline score of 3.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Soft-delete a memory' uses a specific verb ('soft-delete') and a clear resource ('memory'), making its purpose distinct from sibling tools like mnemos_store or mnemos_get. No ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use soft-delete versus alternatives (e.g., permanent deletion, updates, or other operations). The description lacks context for choosing this tool over siblings.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Get a memory by ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes a read operation ('Get'), but annotations set destructiveHint=true, implying mutation or deletion. This is a clear contradiction, and the description does not disclose any behavioral traits beyond what annotations provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no fluff. However, it could be slightly more informative without losing conciseness, hence a 4 rather than 5.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema is provided, and the description does not explain the return format or structure of the memory object. For a retrieval tool, this is a significant omission, leaving the agent uncertain about what data will be returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% for the single required parameter 'id', with description 'Memory ID' already present in the schema. The description adds no extra semantic meaning beyond the schema, meeting the baseline but not exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Get a memory by ID' clearly states the verb (Get), resource (memory), and method (by ID). This distinctly separates it from sibling tools like mnemos_delete or mnemos_store, serving as a straightforward retrieval operation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance provided on when to use this tool versus alternatives (e.g., mnemos_search). The description lacks any context about prerequisites, exclusions, or specific use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Run decay, archival, and GC maintenance
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No | Project scope (empty = all) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already indicate destructiveHint=true, so the description adds some context by naming the specific maintenance operations (decay, archival, GC). However, it does not disclose what gets destroyed, whether changes are reversible, or other behavioral implications beyond the annotation.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. Every part contributes to conveying the tool's purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the basic purpose but lacks details about side effects, return values, or how the parameter affects execution. Given the absence of an output schema and the destructive nature, more context would be beneficial for safe and effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% for the single parameter, so baseline is 3. The description does not add any additional meaning to the parameter beyond what the schema already provides.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'Run' and specifies the resources 'decay, archival, and GC maintenance', which distinguishes it from sibling tools like mnemos_delete or mnemos_store. However, it could be more specific about what each maintenance operation entails.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives, such as when to run maintenance instead of using mnemos_delete or mnemos_update. No context about prerequisites or typical scenarios is given.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Create a relation between two memories
| Name | Required | Description | Default |
|---|---|---|---|
| strength | No | Relation strength [0.0, 1.0] | |
| source_id | Yes | Source memory ID | |
| target_id | Yes | Target memory ID | |
| relation_type | Yes | Relation type: relates_to|depends_on|contradicts|supersedes|derived_from|part_of|caused_by |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate destructiveHint=true and readOnlyHint=false, but the description does not explain whether creating a relation overwrites existing ones or has side effects. The description adds minimal behavioral context beyond what annotations already provide.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single sentence with no wasted words, delivering the core purpose efficiently.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Despite having 4 parameters and no output schema, the description is very brief. It does not explain the effect of the relation, uniqueness constraints, or behavior on duplicates, leaving the agent with significant gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, so the schema fully documents all parameters. The description adds no additional meaning to the parameters, meeting the baseline but not exceeding it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool creates a relation between two memories, which distinguishes it from siblings like mnemos_store (store a memory) or mnemos_delete (delete). However, it could be more specific about what a relation entails in the memory graph.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, such as mnemos_context for contextual links. The description lacks context on prerequisites or exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Report the live MCP server runtime identity: version, host, pid, executable, uptime, data dir, and project scope
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description indicates read-only behavior ('Report'), but annotations set destructiveHint: true, a direct contradiction. The description fails to disclose any actual destructive behavior.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Single sentence, front-loaded with the action and resource, no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Lists all expected return fields, adequate for a simple info tool. No output schema, so description covers main content, though format or example could strengthen it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
No parameters, so baseline 4. Description adds context about what the tool reports (version, host, etc.), compensating for the lack of parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it reports runtime identity and lists specific items (version, host, pid, etc.). Distinct from sibling tools like mnemos_compile, mnemos_delete, etc., which have different purposes.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Implied usage as a diagnostic/info tool, but no explicit guidance on when to use it vs alternatives. No exclusions or prerequisites mentioned.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Search memories using hybrid text+semantic search
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | Search mode: text|semantic|hybrid (default hybrid) | |
| limit | No | Max results (default 10) | |
| query | Yes | Search query | |
| project_id | No | Filter by project |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description describes a read-only search operation, but annotations set readOnlyHint=false and destructiveHint=true, creating a contradiction. The description fails to clarify the actual behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that immediately states the purpose, with no wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a search tool with no output schema, the description is brief and lacks details about return format, pagination, or behavior, though it covers the core functionality.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the description adds no extra meaning beyond the schema. Baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool searches memories and specifies the method (hybrid text+semantic search), distinguishing it from siblings like mnemos_get or mnemos_delete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage for searching but does not explicitly compare to siblings like mnemos_get or mnemos_context, nor does it provide when-not-to-use guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Store a new memory in Mnemos
| Name | Required | Description | Default |
|---|---|---|---|
| tags | No | Comma-separated tags | |
| type | No | Memory type: short_term|long_term|episodic|semantic|skill|compiled | |
| source | No | Source identifier | |
| content | Yes | Memory content (1 byte to 100KB) | |
| summary | No | Optional summary | |
| category | No | Memory category | |
| project_id | No | Project scope |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description says 'Store a new memory' but annotations indicate destructiveHint=true, implying potential data destruction. The description does not address this contradiction or provide any side-effect context, such as overwriting behavior or resource implications.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
At 6 words, it is very concise but may be too brief for a tool with 7 parameters and no output schema. It front-loads the purpose but lacks supporting detail, making it adequately concise but not optimally informative.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity (7 params, no output schema), the description fails to mention return values, confirmation behavior, or the consequences of the 'destructive' annotation. It is incomplete for an agent to use effectively without additional context.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
All 7 parameters have schema descriptions (100% coverage), so the description adds no additional meaning. It does not elaborate on how parameters like 'type' or 'tags' affect storage behavior beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'store' and resource 'memory' in 'Mnemos', indicating a creation operation. However, it does not differentiate from sibling tools like mnemos_update or mnemos_relate, leaving ambiguity about when to use this tool over others.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, exclusions, or context for choosing store over related tools like compile or update.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Update a memory (PATCH semantics)
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Memory ID | |
| tags | No | New comma-separated tags | |
| content | No | New content | |
| summary | No | New summary |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate destructiveHint=true, which aligns with 'Update'. The description adds 'PATCH semantics' but no extra behavioral details (e.g., what happens to unspecified fields, authorization needs). It is minimally adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single sentence that is perfectly concise and front-loaded. Every word contributes essential information (action, resource, update semantics).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a destructive tool with no output schema, the description lacks context about return values, side effects, or behavior on error. More detail would be needed for safe invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema covers all 4 parameters with descriptions (100% coverage). The tool description adds no additional meaning beyond the schema, so baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'Update a memory (PATCH semantics)' clearly states the action (update) and the resource (memory). The parenthetical note adds specificity about partial updates, distinguishing it from siblings like mnemos_store (create) or mnemos_delete.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool vs alternatives like mnemos_get or mnemos_search. No prerequisites or exclusions are mentioned, leaving the agent without decision context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.2.1mnemos_runtimev1.1.14mnemos_compilemnemos_store1 field changedInput schema / properties / type / description"Memory type: short_term|long_term|episodic|semantic"
"Memory type: short_term|long_term|episodic|semantic|skill|compiled"
v0.1.2mnemos_relate1 field changedInput schema / properties / relation_type / description"Relation type"
"Relation type: relates_to|depends_on|contradicts|supersedes|derived_from|part_of|caused_by"
v0.1.0mnemos_contextmnemos_deletemnemos_getmnemos_maintainmnemos_relatemnemos_searchmnemos_storemnemos_updateEach tool has a clearly distinct purpose: CRUD operations (store, get, update, delete), search, maintenance, relation creation, context assembly, compilation, and runtime info. No two tools overlap in functionality, ensuring an agent can easily select the correct tool.
All tools follow a consistent 'mnemos_' prefix with an underscore-separated verb or noun. Most use imperative verbs (compile, delete, get, maintain, relate, search, store, update), while 'context' and 'runtime' are nouns. This minor inconsistency prevents a perfect score.
With 10 tools, the surface is well-scoped for a memory/knowledge server. It covers essential CRUD, search, maintenance, relations, and advanced features like compilation and context assembly without being overwhelming or sparse.
The tool set covers the full memory lifecycle (create, read, update, soft-delete) plus advanced operations (compile, context, relate, maintain, runtime). Minor gaps include missing batch operations or explicit undo for soft-delete, but the core domain is well-served.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Persistent memory for AI agents. Semantic search, memory graph, W3C DID identity.
Persistent memory for AI agents. Search and store durable facts, preferences and decisions.
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
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