obsidian-dev-memory
Obsidian Developer Memory MCP
Un servidor local de Model Context Protocol que proporciona memoria de ingeniería persistente a asistentes de codificación de IA como Cursor y GitHub Copilot.
La memoria se almacena como archivos Markdown ordinarios en un vault de Obsidian. Obsidian no necesita estar en ejecución. No hay plugin de comunidad ni clave de API de Obsidian.
El mismo servidor MCP stdio funciona tanto con Cursor como con GitHub Copilot / VS Code.
Arquitectura
Cursor Agent --------------------\
\
> MCP stdio server
/ |
GitHub Copilot / VS Code --------/ v
obsidian-dev-memory
|
v
Obsidian Markdown VaultDeveloper opens spring-auth in Cursor
|
v
Cursor calls get_project_context("spring-auth")
|
v
AI sees current project state + recent decisions
|
v
Developer and AI implement feature
|
v
AI calls capture_work_session(...)
|
+--> session note
|
+--> Git branch/SHA recorded
|
v
Durable architecture choice?
|
yes
|
v
record_decision(...)Related MCP server: LumenCore
¿Por qué Markdown directo?
El vault es la fuente de verdad. Las notas siguen siendo legibles y editables en Obsidian, git o cualquier editor de texto. El servidor nunca depende de que Obsidian esté abierto, nunca se comunica con una API de memoria alojada y nunca escribe una base de datos propietaria.
Requisitos
Python 3.12+
Un directorio de vault de Obsidian local
Git en
PATHsolo si quieres instantáneas automáticas del repositorio
Instalación
git clone https://github.com/jmjava/obsidian-mcp.git
cd obsidian-mcp
uv syncuv sync instala el SDK oficial de MCP para Python y el paquete del proyecto.
Configuración
Requerido:
export OBSIDIAN_VAULT_PATH="$HOME/Documents/ObsidianVault"Opcional:
export OBSIDIAN_MEMORY_ROOT="AI Memory"OBSIDIAN_MEMORY_ROOT tiene como valor predeterminado AI Memory. La configuración MCP del editor puede suministrar estas variables directamente. Este proyecto incluye .env.example como documentación; el servidor no carga automáticamente archivos .env.
Ejecutar el servidor
export OBSIDIAN_VAULT_PATH="/tmp/example-vault"
mkdir -p "$OBSIDIAN_VAULT_PATH"
uv run python -m obsidian_dev_memoryo:
uv run obsidian-dev-memoryEl proceso habla MCP a través de stdio. No escribas registros de aplicación en stdout; los diagnósticos van a stderr.
Configuración de Cursor
La configuración de Cursor a nivel de proyecto se encuentra en .cursor/mcp.json y utiliza el formato actual mcpServers. Una plantilla portátil está en config/cursor.mcp.json.example:
{
"mcpServers": {
"obsidian-dev-memory": {
"type": "stdio",
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/obsidian-dev-memory-mcp",
"run",
"python",
"-m",
"obsidian_dev_memory"
],
"env": {
"OBSIDIAN_VAULT_PATH": "/ABSOLUTE/PATH/TO/OBSIDIAN/VAULT"
}
}
}
}Este repositorio también incluye .cursor/rules/obsidian-memory.mdc, que le indica a Cursor cuándo leer y escribir memoria.
Los archivos .cursor/mcp.json específicos de cada máquina los crea el instalador y no se confirman aquí.
Configuración de GitHub Copilot / VS Code
La configuración de Copilot / VS Code del espacio de trabajo se encuentra en .vscode/mcp.json y utiliza el formato actual servers. Una plantilla portátil está en config/vscode.mcp.json.example:
{
"servers": {
"obsidian-dev-memory": {
"type": "stdio",
"command": "uv",
"args": [
"--directory",
"/ABSOLUTE/PATH/TO/obsidian-dev-memory-mcp",
"run",
"python",
"-m",
"obsidian_dev_memory"
],
"env": {
"OBSIDIAN_VAULT_PATH": "/ABSOLUTE/PATH/TO/OBSIDIAN/VAULT"
}
}
}
}.github/copilot-instructions.md le da a Copilot el mismo comportamiento de memoria que a Cursor.
Uso del instalador
Conecta este servidor a otro proyecto de desarrollo:
./scripts/install-project.sh \
--project /home/user/src/example \
--vault /home/user/Documents/ObsidianVaultOpcional:
./scripts/install-project.sh \
--project /home/user/src/example \
--vault /home/user/Documents/ObsidianVault \
--server /path/to/obsidian-dev-memory-mcpSi se omite --server, el script infiere este repositorio desde su propia ubicación.
El instalador crea o actualiza:
<project>/.cursor/mcp.json<project>/.cursor/rules/obsidian-memory.mdc<project>/.vscode/mcp.json<project>/.github/copilot-instructions.md
Falla de forma clara cuando falta el proyecto o el vault de destino, y fusiona el JSON de MCP para que no se destruyan servidores no relacionados.
Herramientas MCP
Herramienta | Propósito |
| Lee |
| Añade una sección con marca de tiempo a la nota de sesión de hoy |
| Escribe una nota de decisión duradera |
| Reemplaza la nota concisa de estado del proyecto |
| Búsqueda local de nombres de archivo y texto en la memoria del proyecto |
| Lee un archivo Markdown relativo al vault |
| Añade a |
get_project_context devuelve secciones vacías cuando un proyecto es nuevo en lugar de fallar.
record_decision escribe YYYY-MM-DD-<decision-slug>.md. Si ese archivo ya existe, el servidor añade un sufijo numérico (-2, -3, ...) en lugar de sobrescribirlo.
capture_work_session acepta un repository_path opcional. Cuando esa ruta es un repositorio Git, la nota registra el nombre del repositorio, la rama, el SHA corto, el estado de modificación y una lista breve de archivos modificados. Nunca se escriben diffs completos. Una ruta que no es Git se ignora.
Estructura del vault
AI Memory/
└── Projects/
└── <project-slug>/
├── Project State.md
├── Sessions/
│ └── YYYY-MM-DD.md
└── Decisions/
└── YYYY-MM-DD-<decision-slug>.md
Daily/
└── YYYY-MM-DD.mdLa carpeta AI Memory respeta OBSIDIAN_MEMORY_ROOT. Los nombres lógicos de proyectos se convierten en slugs (Spring Authorization Server → spring-authorization-server).
Flujo de trabajo de ejemplo
Abre un proyecto en Cursor o VS Code.
Antes de un trabajo sustancial, el asistente llama a
get_project_context.Después de una implementación significativa, llama a
capture_work_session.Cuando se toma una decisión de arquitectura, llama a
record_decision.Cuando cambia el estado general, llama a
update_project_state.Abre el vault en Obsidian en cualquier momento para leer o editar los mismos archivos.
Modelo de seguridad
Todas las rutas de notas deben resolverse dentro de
OBSIDIAN_VAULT_PATH.Se rechazan las rutas absolutas, el recorrido
../y las fugas de enlaces simbólicos detectables.Las escrituras son atómicas (
tempfile+os.replace) cuando es práctico.Las herramientas no son una API general de sistema de archivos.
Los valores con aspecto de secreto (claves, tokens, JWT, claves privadas, asignaciones
password=) se reemplazan con[redacted-secret]antes de escribirse.Las reglas de Cursor y las instrucciones de Copilot le indican al asistente que nunca persista contraseñas, claves de API, tokens, JWT, claves privadas, contenido de
.env, credenciales de bases de datos, secretos de producción ni datos sensibles de clientes.
Pruebas
Las pruebas usan directorios temporales, nunca tu vault real.
uv run pytestUna comprobación local más amplia:
export OBSIDIAN_VAULT_PATH="$HOME/Documents/ObsidianVault"
./scripts/smoke-test.shLa prueba de humo verifica la variable de entorno, el directorio del vault, la importación del paquete, la construcción del servidor y la suite de pytest.
Solución de problemas
Síntoma | Qué comprobar |
El servidor sale inmediatamente |
|
Las herramientas no aparecen en Cursor | El |
Las herramientas no aparecen en Copilot | El |
| Pasa rutas relativas al vault como |
El nombre del archivo de decisión ya existía | El servidor escribió |
Falta la sección Git en una sesión | Se omitió |
Ruido inesperado en stdout | Solo MCP JSON-RPC debería usar stdout; los registros van en stderr |
Licencia
MIT. Consulta LICENSE.
Available Tools
7 toolsappend_daily_noteB
Append an item to Daily/YYYY-MM-DD.md without overwriting existing text.
| Name | Required | Description | Default |
|---|---|---|---|
| date | No | ||
| content | Yes | ||
| heading | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It does disclose one important behavior: it appends rather than overwrites. However, it does not explain what happens when the file does not exist, what a null date means, or how the optional heading affects the appended item.
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 filler. It front-loads the action and resource, and the non-destructive guarantee is useful and concise.
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 tool has no annotations and no parameter documentation, so the description needed to compensate. It leaves meaningful gaps: how the optional date and heading parameters behave, whether the daily note file is auto-created, and what the output schema represents. It is enough for a very basic call but not for confident correct use of all parameters.
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 0%, so the description needed to explain the role of 'content', 'date', and 'heading', but it only refers generically to 'an item'. The parameter names are somewhat self-explanatory, but the default/null behavior and the meaning of 'heading' are left entirely to inference.
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 states a specific verb ('Append'), a specific resource ('Daily/YYYY-MM-DD.md'), and a clear non-destructive behavior ('without overwriting existing text'). This makes the tool's purpose immediately distinguishable from sibling read/lookup 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 implies this is the tool to use for adding to a dated daily note, but it gives no explicit guidance about when to choose it over alternatives or when not to use it. No sibling-specific differentiation or exclusion criteria are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
capture_work_sessionA
Capture meaningful work performed during a coding session.
Appends a timestamped section to that day's session note. When repository_path is a Git repo, records branch, short SHA, and dirty state. Never persist secrets or full diffs.
| Name | Required | Description | Default |
|---|---|---|---|
| changes | No | ||
| project | Yes | ||
| summary | Yes | ||
| decisions | No | ||
| next_steps | No | ||
| open_questions | No | ||
| repository_path | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden of disclosing behavior. It reveals that the tool appends a timestamped section, conditionally records Git branch/SHA/dirty state, and never persists secrets or full diffs. It doesn't cover file-creation edge cases or failure behavior, but the core side effects are transparent.
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?
Three short sentences with no filler: purpose, mechanics, and safety constraint. The most important behavioral facts are front-loaded, and every sentence earns its place.
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 7-parameter tool with no annotations and an existing output schema, the description covers purpose, key behavior, Git-related handling, and a safety boundary. The main gap is that it never explains when to use this tool versus the similar sibling append_daily_note, or versus record_decision.
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 0%, so the description must compensate. It does clarify repository_path behavior and constrains 'changes' via the no-full-diffs rule, but it leaves project, summary, decisions, next_steps, and open_questions to be inferred from their names.
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 ('capture') and resource ('that day's session note'), and adds concrete behavioral details like timestamped appending and Git metadata. It doesn't explicitly distinguish itself from the sibling 'append_daily_note', whose name suggests a similar append-to-note 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?
The description gives a clear context: use this to capture meaningful work performed during a coding session. It also implicitly discourages submitting secrets or full diffs. However, it does not state when-not-to-use or name alternatives like append_daily_note or record_decision.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_project_contextA
Retrieve concise durable memory before substantial work.
Reads Project State.md plus the newest session and decision notes. Returns empty sections when the project is new. Never returns the entire vault.
| Name | Required | Description | Default |
|---|---|---|---|
| project | Yes | ||
| recent_sessions | No | ||
| recent_decisions | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full transparency burden, and it discloses key behaviors: it reads three named sources, returns empty sections for new projects, and never returns the entire vault. The guarantee about new projects prevents the agent from misinterpreting missing output as an error. The only gap is what happens when the project argument is invalid or the files are absent, but that is not critical.
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?
Four short sentences present the outcome first, then sources, then edge-case behavior, then a boundary—all relevant and no filler. The line 'Never returns the entire vault' is especially compact and valuable.
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 explains the read behavior and edge case effectively, and an output schema exists so return-structure details are not needed here. However, with three parameters and no schema descriptions, the complete absence of parameter documentation leaves the tool partially underspecified. An agent could call it correctly via defaults, but it would have to guess at the influence of recent_sessions and recent_decisions.
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 0%, so the description must explain the three parameters. It mentions 'Project State.md' and 'newest session and decision notes,' which loosely maps to project and the count parameters, but never states that recent_sessions and recent_decisions control how many notes are read, nor what format project takes. The parameter names are partially self-explanatory, but the description adds no meaningful parameter guidance.
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 opens with 'Retrieve concise durable memory before substantial work,' a specific verb/object pair with a clear trigger condition. It goes on to name the exact sources (Project State.md, newest session and decision notes) and states what it will not do ('Never returns the entire vault'), which separates it from read_note and search_memory.
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 phrase 'before substantial work' supplies an explicit when-to-use signal, positioning the tool as the starting point for context gathering. It does not explicitly name alternatives or exclusions, but the boundary 'Never returns the entire vault' gives agents a cue that this is not a vault-wide read. A stronger definition would name search_memory or read_note as alternatives for broader or note-specific reads.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
read_noteA
Read one Markdown note inside the configured Obsidian vault.
The path must stay inside OBSIDIAN_VAULT_PATH. Absolute paths and traversal such as ../ are rejected.
| Name | Required | Description | Default |
|---|---|---|---|
| path | Yes |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It adds meaningful behavior by stating that paths must stay inside OBSIDIAN_VAULT_PATH and that absolute paths or ../ traversal are rejected. This is more transparent than typical read-tool descriptions, though it does not disclose error behavior or file-not-found handling.
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 two short, purposeful segments: the operation sentence is front-loaded, and the path constraint follows in a compact separate statement. Every sentence earns its place; there is no filler or repetition.
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 read tool, the description plus the existing output schema is largely complete: it states what the tool does and the key input constraint. It leaves minor gaps around expected path format and error handling, but an agent can safely select and invoke the tool based on the provided information.
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 only defines 'path' as a required string with no description, so the description is the sole source of parameter meaning. It adds essential semantics: the path must stay within OBSIDIAN_VAULT_PATH, and absolute or traversal paths are rejected. It could further specify whether a .md extension or relative-to-root syntax is required, but it covers the critical safety constraints.
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 opens with a specific verb and resource: 'Read one Markdown note inside the configured Obsidian vault.' This clearly identifies the operation and the object, and it distinguishes the tool from the sibling tools, which are write/search/update operations.
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 verb 'read' implies this is the tool to use for retrieving a note's content, but the description never states when to prefer it over siblings like search_memory or append_daily_note. There is no explicit when-not-to-use or alternative guidance, so usage is only implied.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
record_decisionA
Store a durable architecture or engineering decision.
Creates YYYY-MM-DD-.md. If that file already exists, a numeric suffix is added instead of overwriting.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| context | Yes | ||
| project | Yes | ||
| decision | Yes | ||
| rationale | No | ||
| alternatives | No | ||
| consequences | No | ||
| related_files | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It clearly discloses that the tool creates a dated markdown file and that existing files are not overwritten but instead get a numeric suffix. This is meaningful side-effect information, even though permissions and exact file location are not mentioned.
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 two short, front-loaded sentences. The first states the purpose and the second explains the exact file behavior and collision handling. There is no filler or redundant restating of the tool name.
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 tool with eight parameters, four required, no annotations, and no parameter descriptions, the definition is too minimal. It covers file creation and overwrite avoidance but leaves unclear how the parameters map to the generated document, when to choose this over sibling tools, and what a valid call should look like.
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 0%, so the description needed to explain the eight parameters, but it only implies a slug derived from the title. It does not clarify project, context, decision, rationale, alternatives, consequences, or related_files. The parameter names are self-explanatory enough to avoid a 1, but the description fails to compensate for the missing schema documentation.
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 opens with a specific verb and resource: 'Store a durable architecture or engineering decision.' It then specifies the concrete artifact, YYYY-MM-DD-<decision-slug>.md, which makes the tool clearly distinct from siblings like capture_work_session, append_daily_note, and search_memory.
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 about when to use record_decision versus the listed alternative tools. The phrase 'durable architecture or engineering decision' implies a use case, but the description does not state exclusions, prerequisites, or how this differs from session capture or daily note appending.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_memoryB
Search project state, sessions, and decisions with local text matching.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| query | Yes | ||
| project | No |
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. 'Local text matching' is a genuine behavioral trait, indicating that search is text-based rather than semantic or remote, but the description does not mention result ordering, read-only behavior, or limitations.
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 filler. It communicates the core action, scope, and mechanism efficiently in nine 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 straightforward search tool, the core purpose and mechanism are present, and an output schema exists to cover return values. However, the lack of usage guidance and parameter semantics leaves the definition minimally viable rather than complete, especially given the absence of annotations.
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 0%, so the description needed to explain the roles of query, limit, and project. It does not; the tool purpose only loosely implies the query parameter, while limit and project remain entirely undocumented in the description.
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 action ('Search') and the resource scope ('project state, sessions, and decisions'), which is specific enough to distinguish it from generic memory tools. It also adds the mechanism 'local text matching' for extra clarity, though it does not explicitly compare against sibling tools like read_note or get_project_context.
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?
There is no guidance about when to use this tool versus its siblings, such as get_project_context or read_note, and no exclusions or alternative recommendations. The intended use is only implied by the name and description, leaving the agent to infer the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_project_stateA
Replace the concise durable Project State.md for a project.
This is current state, not a session log. Omit empty sections.
| Name | Required | Description | Default |
|---|---|---|---|
| notes | No | ||
| blocked | No | ||
| project | Yes | ||
| completed | No | ||
| objective | No | ||
| next_steps | No | ||
| in_progress | No | ||
| architecture | No | ||
| current_state | No | ||
| important_files | No |
Output Schema
| Name | Required | Description |
|---|---|---|
No output parameters | ||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden and it does real work: 'Replace' signals a destructive overwrite of the project state, and 'Omit empty sections' is a concrete behavioral rule for formatting the state. It does not cover permissions or additional side effects, but the key destructive trait is communicated.
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?
Two short sentences with the core action first and the clarifying constraints second. There is no fluff and no repetition of information already present in the input schema.
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?
This is a ten-parameter tool with no schema descriptions and no annotations; the output schema covers only return shape, not usage. The description does not explain how to populate each state section, how to reference the project, or when to choose this over read-only siblings, so the agent is left 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 description coverage is 0%, so the description needed to compensate, but it adds only a generic 'Omit empty sections' rule and no field-level guidance. The project identifier and the intended content of notes/blocked/completed/architecture/etc. are left entirely to inference from parameter titles.
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 opens with a concrete verb ('Replace') and a concrete resource ('Project State.md'), making the tool's action and target unambiguous. Adding 'current state, not a session log' separates it from session-oriented siblings such as capture_work_session.
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 explicitly frames the tool as maintaining current durable state and explicitly rules out session logging, which is the closest alternative behavior among the sibling tools. It does not list every alternative, but the when-to-use signal is clear.
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.
7 tool updates
v0.1.0- First observed
append_daily_note - First observed
capture_work_session - First observed
get_project_context - First observed
read_note - First observed
record_decision - First observed
search_memory - First observed
update_project_state
TDQS
Scored across 7 tools
Each tool targets a distinct memory artifact or action: context retrieval, session capture, decision recording, state updating, searching, reading, and appending to daily notes. Even though several tools involve notes, their purposes are clearly separated.
All tool names follow a consistent lowercase snake_case verb_noun pattern such as capture_work_session, record_decision, and append_daily_note. The naming is predictable and makes the tool's purpose immediately clear.
Seven tools is well-scoped for an Obsidian-based development memory server. Each tool covers a distinct need without unnecessary redundancy or bloat.
The toolkit covers core memory workflows: reading context, capturing sessions, recording decisions, updating state, searching, and appending daily notes. Minor gaps exist around deleting or listing notes, but these are not essential for the stated purpose.
Maintenance
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AlicenseNot gradedqualityCmaintenanceProvides persistent memory for AI coding assistants, storing and retrieving architectural decisions, patterns, and solutions across sessions using semantic search, while also offering git integration for commit messages and code expertise mapping.MIT- AlicenseNot gradedqualityDmaintenanceProvides AI coding assistants with persistent project memory to retain architectural decisions, code patterns, and domain knowledge across sessions. It stores data locally in a SQLite database, allowing agents to remember, recall, and manage project-specific context using full-text search.8 npmApache 2.0
- AlicenseAqualityDmaintenanceEnables AI agents to store and retrieve project context, bugs, decisions, and session logs by reading and appending markdown files in a local Obsidian vault, without requiring any cloud services.6MIT
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