Memstate AI - Agent Memory System
OfficialMemstate AI - MCP
Versionierter Speicher für KI-Agenten. Speichern Sie Fakten, erkennen Sie Konflikte und verfolgen Sie, wie sich Entscheidungen im Laufe der Zeit ändern — bereitgestellt als gehosteter MCP-Server.
Dashboard · Dokumentation · Preise
Warum Memstate?
RAG (die meisten anderen Speichersysteme) | Memstate AI | |
Token-Verbrauch pro Konversation | ~7.500 | ~1.500 |
Sichtbarkeit für den Agenten | Blackbox | Volle Transparenz |
Speicher-Versionierung | Keine | Vollständige Historie |
Token-Wachstum bei Skalierung | O(n) | O(1) |
Erforderliche Infrastruktur | Ja | Keine — gehostetes SaaS |
Andere Speichersysteme laden alles in Ihr Kontextfenster und hoffen auf das Beste. Memstate gibt Ihrem Agenten eine strukturierte, versionierte Wissensdatenbank, die er präzise navigiert — laden Sie nur, was Sie brauchen, wissen Sie, was sich geändert hat, und erkennen Sie, wenn Fakten im Widerspruch stehen.
Related MCP server: BuildAutomata Memory MCP Server
Benchmarks
Wir haben eine Open-Source-Benchmark-Suite erstellt, die testet, was für das Agenten-Gedächtnis wirklich zählt: Kann Ihr System Fakten speichern, sie sitzungsübergreifend präzise abrufen, Konflikte bei Änderungen erkennen und den Kontext bei der Projektentwicklung aufrechterhalten?
Direkter Vergleich: Memstate AI vs. Mem0
Beide Systeme wurden unter identischen Bedingungen mit demselben Agenten (Claude Sonnet 4.6, Temperatur 0), denselben Szenarien und demselben Bewertungsschema getestet.
Metrik | Memstate AI | Mem0 | Gewinner |
Gesamtpunktzahl | 69,1 | 15,4 | Memstate |
Genauigkeit (Faktenabruf) | 74,1 | 12,6 | Memstate |
Konflikterkennung | 85,5 | 19,0 | Memstate |
Kontextkontinuität | 63,7 | 10,1 | Memstate |
Token-Effizienz | 22,3 | 30,6 | Mem0 |
Bewertungsgewichtung: Genauigkeit 40 %, Konflikterkennung 25 %, Kontextkontinuität 25 %, Token-Effizienz 10 %.
Aufschlüsselung nach Szenarien
Der Benchmark führt fünf reale Szenarien aus, die Multi-Session-Agenten-Workflows simulieren:
Szenario | Memstate AI | Mem0 |
Entwicklung der Web-App-Architektur | 43,2 | 55,6 |
Migration des Authentifizierungssystems | 66,2 | 10,2 |
Entwicklung des Datenbankschemas | 72,7 | 7,0 |
API-Versionierungskonflikte | 86,5 | 0,9 |
Umkehrung von Teamentscheidungen | 77,2 | 3,3 |
Mem0 gewann das erste Szenario (einfache Architekturverfolgung), hatte jedoch große Schwierigkeiten bei Szenarien, die den Umgang mit Widersprüchen, sitzungsübergreifenden Kontext und die Verfolgung von Entscheidungsänderungen erforderten — und erzielte bei drei von fünf Szenarien nahezu null Punkte.
Warum Memstate gewinnt
Der Benchmark offenbart einen grundlegenden architektonischen Unterschied:
Mem0 verwendet eine einbettungsbasierte semantische Suche. Fakten werden in Chunks unterteilt, eingebettet und nach Ähnlichkeit abgerufen. Dies funktioniert bei einfachen Abfragen, scheitert jedoch, wenn:
Fakten früheren Fakten widersprechen (das System kann nicht zwischen aktuell und veraltet unterscheiden)
Präziser Abruf erforderlich ist (Einbettungen liefern "ähnliche" Ergebnisse, keine exakten)
Schreib-Lese-Latenz wichtig ist (neue Erinnerungen benötigen Sekunden, um durchsuchbar zu werden)
Memstate verwendet einen strukturierten, versionierten Key-Value-Speicher. Jeder Fakt befindet sich an einem expliziten Keypath mit einer vollständigen Versionshistorie. Das bedeutet:
Konflikterkennung ist integriert — wenn ein neuer Fakt einem alten widerspricht, weiß das System dies und bewahrt beide Versionen auf
Der Abruf ist deterministisch — Sie erhalten genau das zurück, was gespeichert wurde, keine ungefähre Übereinstimmung
Sitzungsübergreifende Kontinuität ist zuverlässig — der Agent navigiert durch einen strukturierten Baum, anstatt darauf zu hoffen, dass die semantische Suche den richtigen Kontext liefert
Token-Kosten bleiben O(1) — der Agent lädt zuerst Zusammenfassungen und geht nur bei Bedarf ins Detail, anstatt alle potenziell relevanten Einbettungen in das Kontextfenster zu laden
Hinweise zur Fairness
Beide Systeme verwendeten dasselbe Agentenmodell, dieselbe Temperatur und dasselbe Bewertungsschema
Mem0 erhielt eine Verzögerung von 10 Sekunden zwischen Schreib- und Lesevorgängen, um die asynchrone Einbettungspipeline zu berücksichtigen
Mem0 erzielt bei der Token-Effizienz höhere Werte, aber diese Metrik sollte im Kontext gelesen werden — ein geringerer Token-Verbrauch kann einfach bedeuten, dass weniger Informationen zurückgegeben werden. Ein System, das unvollständige oder falsche Fakten abruft, verbraucht pro Antwort weniger Token, erfordert aber möglicherweise mehr Folgeanfragen, was letztendlich mehr Token kostet, um zum gleichen Ergebnis zu gelangen
Der Quellcode des Benchmarks ist in diesem Repository zur vollständigen Reproduzierbarkeit enthalten
Mem0 kann bei benutzerdefinierter Konfiguration oder einem anderen Einbettungsmodell eine andere Leistung erbringen
Schnellstart
Holen Sie sich Ihren API-Schlüssel unter memstate.ai/dashboard und fügen Sie ihn dann Ihrer MCP-Client-Konfiguration hinzu:
{
"mcpServers": {
"memstate": {
"command": "npx",
"args": ["-y", "@memstate/mcp"],
"env": {
"MEMSTATE_API_KEY": "YOUR_API_KEY_HERE"
}
}
}
}Kein Docker. Keine Datenbank. Keine Infrastruktur. Läuft in 60 Sekunden.
Client-Einrichtung
Claude Desktop
Speicherort der Konfiguration:
macOS:
~/Library/Application Support/Claude/claude_desktop_config.jsonWindows:
%APPDATA%\Claude\claude_desktop_config.json
{
"mcpServers": {
"memstate": {
"command": "npx",
"args": ["-y", "@memstate/mcp"],
"env": { "MEMSTATE_API_KEY": "YOUR_API_KEY_HERE" }
}
}
}Claude Code
claude mcp add memstate npx @memstate/mcp -e MEMSTATE_API_KEY=YOUR_API_KEY_HERECursor
In Cursor-Einstellungen → MCP → Server hinzufügen — gleiches JSON-Format wie bei Claude Desktop oben.
Cline / Windsurf / Kilo Code / Roo Code
Alle unterstützen das gleiche stdio-MCP-Konfigurationsformat. Fügen Sie es der MCP-Einstellungsdatei Ihres Clients hinzu.
Kern-Tools
Tool | Wann zu verwenden |
| Speichern von Markdown, Aufgabenzusammenfassungen, Entscheidungen. Der Server extrahiert Keypaths und erkennt Konflikte automatisch. Für die meisten Schreibvorgänge verwenden. |
| Setzt einen einzelnen Keypath auf einen kurzen Wert (z. B. |
| Durchsuchen aller Erinnerungen für ein Projekt oder einen Teilbaum. Zu Beginn jeder Aufgabe verwenden. |
| Semantische Suche nach Bedeutung, wenn Sie den genauen Keypath nicht kennen. |
| Sehen Sie, wie sich ein Wissensteil im Laufe der Zeit verändert hat — vollständige Versionskette. |
| Soft-Delete eines Keypaths. Erstellt einen Platzhalter; die vollständige Historie bleibt erhalten. |
| Soft-Delete eines gesamten Projekts und aller seiner Erinnerungen. |
Funktionsweise von Keypaths
Erinnerungen sind in einer hierarchischen Punkt-Notation organisiert:
project.my_app.database.schema
project.my_app.auth.provider
project.my_app.deploy.environmentKeypaths werden automatisch mit einem Präfix versehen: keypath="database" mit project_id="my_app" → project.my_app.database. Ihr Agent kann genau auf das zugreifen, was er benötigt — keine Dumps des gesamten Kontexts.
Funktionsweise
Agent: memstate_remember(project_id="my_app", content="## Auth\nUsing SuperTokens...")
↓
Server extracts keypaths: [project.my_app.auth.provider, ...]
↓
Conflict detection: compare against existing memories at those keypaths
↓
New version stored — old version preserved in history chain
↓
Next session: memstate_get(project_id="my_app") → structured summaries only
↓
Agent drills into project.my_app.auth only when it needs auth detailsDie Token-Kosten bleiben konstant, unabhängig davon, wie viele Erinnerungen insgesamt existieren.
Zu Ihren Agenten-Anweisungen hinzufügen
Kopieren Sie dies in Ihre AGENTS.md oder Ihren System-Prompt:
## Memory (Memstate MCP)
### Before each task
- memstate_get(project_id="my_project") — browse existing knowledge
- memstate_search(query="topic", project_id="my_project") — find by meaning
### After each task
- memstate_remember(project_id="my_project", content="## Summary\n- ...", source="agent")
### Tool guide
- memstate_remember — markdown summaries, decisions, task results (preferred)
- memstate_set — single short values only (config flags, status)
- memstate_get — browse/retrieve before tasks
- memstate_search — semantic lookup when keypath unknown
- memstate_history — audit how knowledge evolved
- memstate_delete — remove outdated memories (history preserved)Umgebungsvariablen
Variable | Standard | Beschreibung |
| (erforderlich) | API-Schlüssel von memstate.ai/dashboard |
|
| Überschreibung für selbst gehostete Bereitstellungen |
Verbindung überprüfen
MEMSTATE_API_KEY=your_key npx @memstate/mcp --testListet alle verfügbaren Tools auf und bestätigt, dass Ihr API-Schlüssel funktioniert.
Entwickelt für KI-Agenten, die es verdienen zu wissen, was sie wissen.
Available Tools
7 toolsmemstate_deleteA
Soft-delete a memory by keypath. Creates a tombstone version preserving full history. The memory can be un-deleted by setting a new value at the same keypath.
USE THIS WHEN: You need to remove outdated or incorrect memories, clean up a keypath subtree, or mark memories as no longer relevant. NOT FOR: Updating content (use memstate_set or memstate_remember to overwrite with new content instead).
memstate_delete(project_id="myapp", keypath="config.old_setting") memstate_delete(project_id="myapp", keypath="config", recursive=true) → deletes config and all children
History is preserved. Use memstate_history to see the deletion in the version chain. Keypath is auto-prefixed.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Required. Project containing the memory. | |
| keypath | Yes | Required. Keypath to delete (auto-prefixed with 'project.{project_id}.'). | |
| recursive | No | If true, delete the entire keypath subtree. Default: false. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: soft-deletion (not permanent), tombstone creation, history preservation, undeletion capability, and auto-prefixing behavior. It also references memstate_history for viewing deletions. The only minor gap is lack of explicit mention about permissions or error conditions.
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 well-structured with clear sections, uses bold headings for emphasis, includes practical examples, and every sentence adds value. It's appropriately sized for a tool with important behavioral nuances and sibling distinctions.
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 tool's complexity (soft-deletion with history preservation), the description provides comprehensive context. With no annotations but an output schema present, it covers behavioral aspects thoroughly while appropriately deferring return value details to the output schema. It addresses key sibling relationships and usage scenarios.
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 baseline is 3. The description adds some value by providing concrete examples with project_id='myapp' and showing recursive behavior, but doesn't significantly enhance the parameter understanding beyond what the schema already documents clearly.
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 specific action ('soft-delete a memory by keypath') and distinguishes it from siblings by explaining it creates tombstone versions and preserves history. It explicitly contrasts with memstate_set and memstate_remember for updating content.
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 includes explicit 'USE THIS WHEN' and 'NOT FOR' sections that provide clear guidance on when to use this tool versus alternatives. It mentions specific scenarios (removing outdated memories, cleaning up subtrees) and names alternative tools for different use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memstate_delete_projectA
Soft-delete an entire project and all its memories. Creates tombstone versions for every memory, preserving full history. The project is hidden from listings but can be restored by creating a new project with the same ID.
USE THIS WHEN: You want to remove all memories for a project, e.g. cleaning up test data or decommissioning a project.
memstate_delete_project(project_id="old-project")
All memories get individual tombstone versions. History is preserved via memstate_history.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Required. Project ID to soft-delete. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden and does so well. It discloses key behavioral traits: it's a soft-delete (not permanent), creates tombstone versions for all memories, preserves history, hides the project from listings, and can be restored by creating a new project with the same ID. It doesn't mention rate limits or auth needs, but covers the essential destructive nature and recovery mechanism adequately.
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 appropriately sized and front-loaded with the core action. The 'USE THIS WHEN' section is helpful but slightly repetitive of the purpose. The example usage is concise and adds clarity. Minor improvements could tighten it, but overall it's efficient with minimal waste.
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 tool's complexity (destructive operation with recovery), no annotations, and an output schema (which handles return values), the description is complete. It covers purpose, usage, behavior, and parameters adequately, providing all necessary context for an agent to invoke it correctly without over-explaining.
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?
The schema description coverage is 100%, so the baseline is 3. The description adds value by providing an example usage (memstate_delete_project(project_id='old-project')) which clarifies the parameter's role in context, though it doesn't add deep semantic details beyond what the schema already states ('Required. Project ID to soft-delete.').
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 specific action ('soft-delete an entire project and all its memories') and distinguishes it from siblings like memstate_delete (which likely deletes individual memories) by specifying it affects the entire project. It explains the outcome (creates tombstone versions, preserves history, hides from listings) which clarifies the scope beyond just the name.
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?
It includes an explicit 'USE THIS WHEN' section that provides clear guidance: 'You want to remove all memories for a project, e.g. cleaning up test data or decommissioning a project.' This directly tells the agent when to use this tool versus alternatives, with practical examples.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memstate_getA
Browse and retrieve memories. Use BEFORE starting tasks to fetch existing knowledge.
USE THIS WHEN: You need to read what's already stored — list projects, browse a project tree, or get content at a keypath. NOT FOR: Saving new content (use memstate_remember for markdown/summaries, memstate_set for one keypath value).
memstate_get() → list all projects memstate_get(project_id="myapp") → full project tree memstate_get(project_id="myapp", keypath="database") → subtree memstate_get(project_id="myapp", keypath="db", include_content=true) → with full content memstate_get(memory_id="mem_abc123") → single memory by ID
Keypath is auto-prefixed. Default recursive=true.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No | Project to retrieve from. If omitted, lists all projects. | |
| keypath | No | Subkeypath within project. If omitted with project_id, returns the full project tree. | |
| memory_id | No | Alternative: get a single memory by its UUID | |
| recursive | No | Include subtree (default: true) | |
| include_content | No | If true, include full memory content in response (not just keypaths) | |
| at_revision | No | Optional revision number for time-travel queries | |
| content_limit | No | Max memories to hydrate when include_content=true (default: 200, max: 1000) |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and adds valuable behavioral context. It explains that 'Keypath is auto-prefixed' and 'Default recursive=true', which are important behavioral traits not evident from the schema alone. It also provides example usage patterns showing different parameter combinations and their effects.
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 well-structured with clear sections, bullet points for examples, and zero wasted text. Every sentence serves a purpose: establishing context, providing usage guidelines, showing examples, and noting important defaults. It's appropriately sized for a tool with 7 parameters and complex usage patterns.
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 tool's complexity (7 parameters, multiple usage patterns) and the presence of an output schema, the description is complete. It covers purpose, guidelines, behavioral traits, and provides concrete examples. The output schema existence means the description doesn't need to explain return values, and it adequately addresses the tool's scope and limitations.
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 baseline is 3. The description adds some value through the usage examples that show how parameters combine (e.g., project_id alone vs. with keypath, include_content=true), but doesn't provide significant additional semantic meaning beyond what's already documented in the schema descriptions.
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's purpose: 'Browse and retrieve memories' with specific verbs and resources. It distinguishes from siblings by explicitly contrasting with memstate_remember (for saving markdown/summaries) and memstate_set (for one keypath value), making the distinction clear.
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 explicit guidance: 'Use BEFORE starting tasks to fetch existing knowledge' and includes dedicated 'USE THIS WHEN:' and 'NOT FOR:' sections. It names specific alternatives (memstate_remember, memstate_set) and gives concrete scenarios for when to use this tool (list projects, browse project tree, get content at keypath).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memstate_historyA
View version history for a keypath or memory chain. Use when you need to see how a value changed over time.
USE THIS WHEN: Debugging past state, auditing changes, or recovering a previous value. NOT FOR: Reading current content (use memstate_get) or saving (use memstate_remember or memstate_set).
memstate_history(project_id="myapp", keypath="config.database.port") memstate_history(memory_id="mem_abc123")
Returns all versions with timestamps; latest is marked is_latest=true.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | No | Required with keypath | |
| keypath | No | Get history for this keypath | |
| memory_id | No | Or get history for a specific memory chain by ID |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: it returns 'all versions with timestamps' and marks the latest with 'is_latest=true.' However, it lacks details on potential limitations like rate limits, pagination, or error conditions, which would be helpful for a read operation with historical data.
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 well-structured and front-loaded: it starts with the core purpose, followed by usage guidelines, examples, and return details. Every sentence adds value—no waste. The use of bold headers and code examples improves readability without unnecessary verbosity.
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 tool's moderate complexity (3 parameters, no annotations, but with an output schema), the description is complete enough. It covers purpose, usage, parameters, and return values. Since an output schema exists, the description doesn't need to explain return values in detail, and it adequately addresses the context provided by sibling tools and schema coverage.
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?
The input schema has 100% description coverage, so the baseline is 3. The description adds value by clarifying parameter semantics: it explains that 'project_id' is 'Required with keypath' (though this is also in the schema) and provides example usage with both 'keypath' and 'memory_id', showing how they are used alternatively. This enhances understanding beyond the schema's basic descriptions.
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's purpose: 'View version history for a keypath or memory chain.' It specifies the verb ('view') and resource ('version history'), and distinguishes it from siblings by explicitly naming alternatives (memstate_get for current content, memstate_remember/memstate_set for saving). This is specific and avoids tautology.
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 explicit usage guidelines with dedicated sections: 'USE THIS WHEN:' lists debugging, auditing, and recovery scenarios, and 'NOT FOR:' explicitly names when not to use it (reading current content or saving) and specifies alternative tools (memstate_get, memstate_remember, memstate_set). This gives clear context and exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memstate_rememberA
Save markdown, task summaries, or any text. Server extracts keypaths and creates structured memories automatically. This is the PREFERRED way to save information.
USE THIS WHEN: Saving task summaries, meeting notes, docs, or any text with multiple facts. The server handles organization, conflict detection, and versioning. NOT FOR: Setting one specific keypath to a short value (e.g. config.port = "8080") — use memstate_set for that.
memstate_remember(project_id="myapp", content="## Task Summary\n- Added OAuth\n- Files: auth.go, middleware.go", source="agent") memstate_remember(project_id="myapp", content="Architecture decision: migrated to JWT tokens for session management")
Content limit: 100,000 chars. Processing is async (~15-18s); returns job_id immediately.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Required. Project to store in (e.g. 'myapp'). Auto-creates if new. | |
| content | Yes | Markdown or text to remember (max 100,000 chars). Server extracts keypaths and creates structured memories automatically. | |
| source | No | Source type: agent, readme, docs, meeting, code | |
| context | No | Optional hint to guide keypath extraction |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: content limit (100,000 chars), async processing with timing (~15-18s), immediate job_id return, automatic project creation, and server-side organization/conflict detection/versioning. It doesn't mention error conditions or retry behavior, keeping it from a perfect score.
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 efficiently structured with clear sections, front-loaded key information, and every sentence adds value. It uses bold headings for guidance, includes practical examples, and provides critical constraints without unnecessary elaboration.
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 tool's complexity (async processing, automatic extraction), 100% schema coverage, and presence of an output schema, the description provides excellent context. It covers purpose, usage guidelines, behavioral traits, constraints, and examples, making it complete enough for an agent to understand when and how to use this tool effectively.
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 baseline is 3. The description adds minimal parameter semantics beyond the schema: it reinforces content limits and provides example values in the code snippets, but doesn't significantly enhance understanding of parameters beyond what the schema already documents well.
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's purpose: 'Save markdown, task summaries, or any text' with server-side extraction of keypaths and structured memory creation. It explicitly distinguishes from sibling tools by stating this is the 'PREFERRED way to save information' and contrasting with memstate_set for specific keypath assignments.
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 explicit guidance with dedicated 'USE THIS WHEN:' and 'NOT FOR:' sections, listing specific use cases (task summaries, meeting notes, docs) and clear exclusions (setting one specific keypath to short values). It names the alternative tool (memstate_set) for excluded scenarios.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memstate_searchA
Find memories by meaning (semantic search). Use BEFORE starting tasks when you don't know the exact keypath.
USE THIS WHEN: You want to find relevant memories by topic or meaning, not by exact keypath. NOT FOR: Saving content (use memstate_remember for markdown/summaries, memstate_set for one keypath value).
memstate_search(query="how is authentication configured", project_id="myapp") memstate_search(query="database connection settings", project_id="myapp")
Returns summaries with similarity scores. Use memstate_get(memory_id="...") to fetch full content of a result.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Natural language search query. Leave empty to explore/list all memories ordered by keypath. | |
| project_id | No | Filter by project | |
| limit | No | Maximum results (default: 20, max: 100) | |
| categories | No | Filter by categories | |
| keypath_prefix | No | Filter by keypath prefix | |
| include_superseded | No | Include old versions |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a search tool (implied read-only), returns summaries with similarity scores, and requires memstate_get to fetch full content. However, it doesn't mention potential limitations like rate limits or authentication needs, which could be relevant for a search tool.
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 well-structured and front-loaded with the core purpose. Every sentence adds value: the initial statement, usage guidelines, examples, and instructions for fetching full content. It uses bold formatting effectively without being verbose, and there's no wasted text.
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 tool's complexity (semantic search with 6 parameters), no annotations, but with a rich input schema (100% coverage) and output schema (confirmed present), the description is mostly complete. It explains the tool's purpose, usage, and result handling well. The main gap is lack of explicit behavioral details like error conditions or performance characteristics, but the output schema likely covers return values.
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 documents all 6 parameters thoroughly. The description adds minimal parameter semantics beyond the schema—it only mentions 'query' and 'project_id' in examples. This meets the baseline of 3 for high schema coverage, but doesn't provide additional meaningful context about 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?
The description clearly states the tool's purpose: 'Find memories by meaning (semantic search).' It specifies the verb ('Find'), resource ('memories'), and method ('semantic search'), and distinguishes it from siblings by contrasting with exact keypath searches. The title being null doesn't detract from this clarity.
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 explicit guidance with 'USE THIS WHEN:' and 'NOT FOR:' sections, clearly stating when to use this tool (for semantic search when exact keypath is unknown) and when to use alternatives (memstate_remember for saving content, memstate_set for one keypath value). It also mentions using it 'BEFORE starting tasks' for context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memstate_setA
Set one keypath to a short value. Only for simple key=value facts like config, status, or version numbers.
USE THIS WHEN: You need to store ONE specific value at a known keypath (e.g. a port number, a status flag, a version string). NOT FOR: Task summaries, markdown, or text with multiple facts — use memstate_remember instead.
memstate_set(project_id="myapp", keypath="config.database.port", value="5432") memstate_set(project_id="myapp", keypath="status.deployment", value="production") memstate_set(project_id="myapp", keypath="version.current", value="2.1.0")
Value limit: 2,000 chars. If longer, use memstate_remember instead. Keypath is auto-prefixed.
| Name | Required | Description | Default |
|---|---|---|---|
| project_id | Yes | Required. Project to store in (e.g. 'myapp'). Auto-creates if new. | |
| keypath | Yes | Required. Hierarchical path like 'config.port' or 'status'. Auto-prefixed with 'project.{project_id}.' | |
| value | Yes | The value to store (max 2,000 chars). Short, simple values only — not markdown. | |
| category | No | Category: decision, preference, fact, task, context, requirement, note, code, learning | |
| topics | No | Optional additional topics/tags |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and delivers well. It discloses key behavioral traits: value limit (2,000 chars), auto-prefixing of keypath, and that it's for storage/mutation (implied by 'set'). It doesn't mention error conditions, permissions, or rate limits, but provides substantial operational context beyond basic function.
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 perfectly structured and concise. It opens with the core purpose, provides clear usage guidelines in dedicated sections, includes practical examples, and adds important constraints. Every sentence earns its place with no wasted words, and information is front-loaded appropriately.
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 tool's moderate complexity (storage operation with 5 parameters), 100% schema coverage, and presence of an output schema, the description is complete. It covers purpose, usage guidelines, constraints, and provides examples. The output schema means return values don't need explanation in the description.
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 baseline is 3. The description adds minimal parameter semantics beyond the schema: it reinforces that value should be 'short, simple values only' and provides concrete examples of keypath usage. However, it doesn't explain the optional 'category' parameter's purpose or provide guidance on when to use topics.
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's purpose: 'Set one keypath to a short value' with specific examples of what to store (config, status, version numbers). It explicitly distinguishes from sibling memstate_remember by stating 'Only for simple key=value facts' and 'NOT FOR: Task summaries, markdown, or text with multiple facts.'
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 explicit usage guidance with dedicated sections: 'USE THIS WHEN:' for appropriate scenarios (storing one specific value at known keypath) and 'NOT FOR:' for exclusions (task summaries, markdown, multi-fact text). It names the alternative tool (memstate_remember) and includes a value length threshold (2,000 chars) for when to switch tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v1.0.2- Changed
memstate_search1 field changed- changed
Input schema / properties / query / descriptionPrevious value: -"Natural language search query"New value: +"Natural language search query. Leave empty to explore/list all memories ordered by keypath."
7 tool updates
v1.0.1- First observed
memstate_delete - First observed
memstate_delete_project - First observed
memstate_get - First observed
memstate_history - First observed
memstate_remember - First observed
memstate_search - First observed
memstate_set
TDQS
Scored across 7 tools
Each tool has a clearly distinct purpose with minimal overlap. The descriptions explicitly differentiate tools (e.g., memstate_remember vs. memstate_set, memstate_get vs. memstate_search) and include 'USE THIS WHEN' and 'NOT FOR' sections that prevent confusion. The tools cover different aspects of memory management without redundancy.
All tools follow a consistent 'memstate_verb' naming pattern (e.g., memstate_delete, memstate_get, memstate_search). This uniformity makes it easy to identify the server's tools and their functions at a glance, with no deviations in style or structure across the set.
With 7 tools, the server is well-scoped for an agent memory system. Each tool serves a specific, necessary function (e.g., create, read, update, delete, search, history, project management), and there are no extraneous tools. The count aligns with the domain's complexity without being overwhelming or insufficient.
The tool set provides comprehensive coverage for memory management, including CRUD operations (memstate_remember/set for create/update, memstate_get for read, memstate_delete for delete), search (memstate_search), version history (memstate_history), and project-level management (memstate_delete_project). There are no obvious gaps; agents can handle full memory lifecycles and workflows effectively.
Maintenance
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