Die Autopilot-Wissensdatenbank für Ihren Coding-Agenten.
Einmal installieren. Von da an baut Ihr Agent während Sie programmieren selbstständig eine strukturierte Wissensdatenbank Ihres Projekts auf. Keine Prompts, die man sich merken muss, keine remember()-Aufrufe, keine API, die man lernen muss.
Ein einzelnes Go-Binary. Eingebettetes SQLite. Keine Cloud. Kein Docker. Kein Python. Keine Node-Laufzeitumgebung.
Agent (Claude Code / Cursor / Kiro / Gemini CLI / ...)
↓ MCP stdio
mnemos serve
↓
Auto-compiled knowledge base (~/.mnemos/mnemos.db)Jeder Memory-Server speichert Text. Mnemos kompiliert eine Wissensdatenbank.
Während andere Server erwarten, dass Sie (oder ein sorgfältig abgestimmter Prompt) entscheiden, wann gespeichert und wann abgerufen werden soll, führt mnemos im Hintergrund eine vollständige Pipeline aus:
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 / working)
├── 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)Sie rufen nichts davon auf. Ihr Agent ruft nichts davon auf. Hooks lösen dies automatisch bei Sitzungsbeginn, Prompt-Absendung und Sitzungsende aus.
Related MCP server: Gingugu
Ebene 1 — MCP-Transport. Standard-MCP-Server, stdio, funktioniert mit jedem MCP-Client.
Ebene 2 — Autopilot-Hooks. Ein Befehl (mnemos setup claude) verknüpft Hooks + Steuerung + MCP-Konfiguration. Sitzungsstart injiziert automatisch relevanten Kontext. Prompt-Absendung sucht bei Themenwechsel automatisch. Sitzungsende verifiziert die Abdeckung.
Ebene 3 — Automatisch kompilierte Wissensdatenbank. Qualitäts-Gate, 3-stufige Deduplizierung, automatische Zusammenfassung, Dateiverknüpfung, MMR-Kontext-Assemblierung — alles automatisch. Sie lösen dies nie manuell aus. Enthält einen passiven Hintergrund-Daemon, der kontinuierlich Veralterung, Widersprüche und fehlende Beziehungen in Ihrer Memory-Basis erkennt.
Mem0 | Zep/Graphiti | engram | OMEGA | mnemos | |
MCP-nativ | ✓ | ✓ | ✓ | ✓ | ✓ |
Einzelnes Binary, keine Laufzeit-Deps | — | — | ✓ | — | ✓ |
Keine Cloud / Local-First | teilweise | — | ✓ | ✓ | ✓ |
1-Befehl Autopilot-Setup | — | — | — | — | ✓ |
Auto-Qualitäts-Gate | — | — | — | — | ✓ |
Auto-Zusammenfassung | — | — | — | — | ✓ |
Auto-Dateiverknüpfung (git-aware) | — | — | — | — | ✓ |
MMR-Kontext-Assemblierung | — | — | — | — | ✓ |
Passiver Hintergrund-Daemon | — | — | — | — | ✓ |
Temporaler Wissensgraph | — | ✓ | — | — | teilweise (Decay + Ersetzung) |
Self-Host-Kosten | $0-Cloud | ~$50/Monat (Neo4j) | $0 | $0 | $0 |
Mnemos versucht nicht, Zep zu sein — das ist eine andere Wette. Zep ist die beste Antwort, wenn Sie temporale Schlussfolgerungen über Geschäftsfakten benötigen und über eine Unternehmens-Infrastruktur verfügen. Mnemos ist die beste Antwort, wenn Sie ein Coding-Agent-Nutzer sind, der eine Autopilot-Wissensdatenbank möchte, die auf Ihrem Laptop von selbst läuft.
# 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 (coming in v1.2)
# npx mnemos setup claude
# Build from source (requires Go 1.23+)
git clone https://github.com/s60yucca/mnemos
cd mnemos && make buildTauschen Sie claude gegen cursor, kiro oder gemini-cli aus. Starten Sie Ihren Client neu. Der Autopilot läuft ab hier.
mnemos setup <client> schreibt:
Steuerungsdatei (CLAUDE.md, .cursorrules, .kiro/steering/mnemos.md) — sagt dem Agenten, was speicherenswert ist
Hook-Konfiguration (.claude/hooks.json oder äquivalent) — verknüpft Lebenszyklus-Ereignisse
MCP-Konfiguration (.mcp.json) — registriert mnemos serve als Tool-Provider
Drei Hooks laufen automatisch:
Sitzungsstart → mnemos hook session-start
Stellt relevanten Kontext innerhalb eines Token-Budgets zusammen (MMR-diversifiziert, datei-priorisiert). Injiziert diesen in den Kontext. Kaltstart < 200 ms.
Prompt-Absendung → mnemos hook prompt-submit
Erkennt Themen- und Absichtsänderungen. Durchsucht automatisch die Wissensdatenbank, wenn die Verschiebung sinnvoll ist. Respektiert Abklingzeiten, um Rauschen zu vermeiden.
Sitzungsende → mnemos hook session-end
Verifiziert, ob dauerhafte Erinnerungen erfasst wurden. Speichert optional einen minimalen Breadcrumb. Bereinigt den Sitzungsstatus.
Die Steuerung sagt dem Agenten, was es wert ist, sich daran zu erinnern. Hooks übernehmen das Abrufen, Deduplizieren, Zusammenfassen und Verknüpfen — damit der Agent keine Token mit der Logistik der Erinnerungen verschwendet.
Über die Hooks hinaus führt mnemos einen Hintergrund-Daemon aus, der Ihre Wissensdatenbank kontinuierlich verbessert:
Veralterungserkennung — markiert Erinnerungen, die auf gelöschte Dateien oder veraltete Muster verweisen
Widerspruchserkennung — findet Erinnerungen, die sich gegenseitig widersprechen
Beziehungsinferenz — verknüpft automatisch verwandte Erinnerungen
Backfill — generiert rückwirkend Zusammenfassungen für Erinnerungen, denen diese fehlen
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 findingsOperation | 350 Erinnerungen | 1.500 Erinnerungen |
| 57 ms | 24 ms |
| 55 ms | 22 ms |
| 42 ms | 39 ms |
| 27 ms | 108 ms |
Hook session-start (kalt) | < 200 ms | — |
Binary-Größe | ~12 MB | — |
Hardware: M1 Pro, 16GB RAM, SQLite auf SSD. Ihre Latenz kann variieren.
Die meisten Operationen bleiben unabhängig von der Datensatzgröße unter 60 ms. Hook-Unterbefehle verwenden den InitLight-Modus — keine Hintergrund-Worker, keine Sitzungsunterbrechung.
Der Wert-Benchmark (Token-Einsparungen, Präzision, Vermeidung von Fallstricken) ist in Arbeit. Siehe DOGFOODING_RUNBOOK.md für die Methodik. Echte Zahlen werden diesen Platzhalter vor der öffentlichen Veröffentlichung ersetzen.
Tool | Was es tut |
| Speichert eine Erinnerung (volle Auto-Pipeline läuft transparent) |
| Hybride FTS + semantische + Datei-Überlappungssuche mit MMR |
| Stellt budgetbewussten, diversifizierten Kontext für den Sitzungsstart zusammen |
| Abrufen nach ID |
| Inhalt, Zusammenfassung oder Tags aktualisieren |
| Soft-Delete (wiederherstellbar über maintain) |
| Verknüpft zwei Erinnerungen (supersedes, caused_by, depends_on) |
| Führt Decay, Archivierung, GC, Veralterungserkennung aus |
# 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 maintainDie meisten Benutzer berühren dies nie. Aber falls Sie möchten:
# ~/.mnemos/config.yaml
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: falseMnemos klassifiziert automatisch. Überschreiben Sie dies manuell über das --type-Flag.
Typ | Decay-Rate | Verwendung für |
| schnell (~1 Tag) | Todos, temporäre Notizen, WIP |
| mittel (~1 Monat) | Sitzungsereignisse, Bugfixes |
| langsam (~6 Monate) | Architektur-Entscheidungen |
| sehr langsam | Fakten, Definitionen, Wissen |
| schnell | aktiver Aufgabenkontext |
Ich war es leid, Claude Code jeden Morgen mein eigenes Projekt neu zu erklären.
Ich habe die existierenden Memory-Server ausprobiert. Die meisten speicherten Text gut. Aber jeder erwartete von mir — oder einem sorgfältig abgestimmten Prompt —, dass ich entscheide, wann gespeichert und wann abgerufen werden soll. Das ist keine Wissensdatenbank. Das ist eine Datenbank mit einem MCP-Wrapper.
Mnemos ist das, was ich gebaut habe, um es tatsächlich automatisch zu machen. mnemos setup claude, Editor neu starten, und die Wissensdatenbank kompiliert sich von selbst.
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.jsonFlags: --global (für alle Projekte installieren), --force (bestehende überschreiben).
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 serve # MCP server (stdio)
mnemos version
# Autopilot setup
mnemos setup claude | cursor | kiro | gemini-cli [--global] [--force]
# 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]
# Hook subcommands (called by clients, not manually)
mnemos hook session-start
mnemos hook prompt-submit
mnemos hook session-endKein Chatbot-Memory-SaaS. Für den Abruf von Benutzerpräferenzen in Kundensupport-Bots verwenden Sie Mem0.
Keine temporale Wissensgraph-Datenbank. Für gültig-ab/ungültig-ab-Schlussfolgerungen über Geschäftsfakten verwenden Sie Zep.
Kein Cloud-Produkt. Es gibt keine mnemos-Cloud. Es wird niemals eine geben.
Nicht Framework-spezifisch. MCP-nativ. Funktioniert mit allem, was MCP spricht.
Mnemos tut eine Sache: Agenten eine Wissensdatenbank geben, die sich selbst kompiliert.
Siehe ROADMAP.md. Kurzfassung:
v1.1.1 (veröffentlicht): vollständige Auto-Pipeline, MMR-Kontext-Assemblierung, dateibewusster Abruf, passiver Autopilot-Daemon, Benchmark-Framework
v1.2 (nächste): öffentlicher Wert-Benchmark (Dogfooding), npm-Wrapper, Demo-GIF, HN-Launch
v1.3 (geplant): Team-Memory via Git — geteiltes .mnemos/shared/ für Teamkollegen-Wissen
v2.0+ (TBD): projektübergreifende Memory-Scopes, Memory-Kompaktierung, gesteuert durch Benutzerfeedback
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 / descriptionPrevious value: -"Memory type: short_term|long_term|episodic|semantic"New value: +"Memory type: short_term|long_term|episodic|semantic|skill|compiled"v0.1.2mnemos_relate1 field changedInput schema / properties / relation_type / descriptionPrevious value: -"Relation type"New value: +"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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