Notes MCP
MCP Eval Demo
Un ejemplo práctico de cómo usar evaluaciones para verificar que un agente de LLM realmente puede usar de forma eficaz un servidor MCP — no solo que el código del servidor es correcto.
Las pruebas unitarias responden a «¿delete_note borra una nota?». No pueden responder a las preguntas que deciden si un servidor MCP es bueno en la práctica:
¿Encuentra el agente la nota correcta cuando el usuario la describe con palabras en lugar de por id?
¿Se da cuenta de que una vista previa del listado estaba truncada, o responde a partir de media nota?
¿Se da cuenta de que
update_notesobrescribe, o destruye en silencio el contenido del usuario cuando le pide “añade una línea a mi lista de la compra”?¿Se recupera de un mensaje de error o se rinde?
Esas son propiedades de la superficie de las herramientas — nombres, descripciones, esquemas, formas de los resultados, texto de error — y la única manera de comprobarlas es ejecutar un agente real contra el servidor y evaluar lo que ha hecho. Para eso está este repositorio.
Estado
El servidor MCP, su infraestructura y el harness de evaluación ya están en su sitio.
Related MCP server: notes-mcp-server
El servidor bajo prueba: Notes MCP
Un cuaderno en memoria. El estado reside en el proceso del servidor y se descarta al salir, por lo que cada ejecución de evaluación parte del mismo corpus conocido (consulta seed.py).
Herramienta | Comportamiento | Qué hace |
| escritura | Crea una nota; los títulos deben ser únicos sin distinguir entre mayúsculas y minúsculas. |
| solo lectura | Devuelve el contenido completo de una nota, por id. |
| solo lectura | Lista las notas de la más reciente a la más antigua, como vistas previas truncadas, con un |
| destructivo | Sobrescribe el título y/o el contenido de una nota. |
| destructivo | Elimina una nota de forma permanente. |
Hay varias decisiones de diseño pensadas expresamente para que las evaluaciones tengan algo que detectar:
Ids, no títulos. Toda herramienta de mutación toma un
note_id, así que un agente al que se le pide cambiar “mi lista de la compra” debe buscar el id antes. Ahí es donde los agentes suelen tomar la solución.Vistas previas truncadas.
list_notesdevuelve solo los primeros 120 caracteres de cada nota, marcadas concontent_truncatedycontent_length. Un agente que responde una pregunta de contenido directamente desde un listado falla; un buen agente llama aget_note.Reemplaza, no añade.
update_notesobrescribe. “Añade huevos a mi lista de la compra” es, por tanto, una operación de lectura-modificación-escritura, y un agente que se salta la lectura destruye datos.Errores que enseñan. Cada fallo nombra el valor problemático y señala la herramienta que lo resolvería, de modo que el agente tiene una salida y no un punto muerto.
Estructura
src/notes_mcp/
models.py Pydantic models — also the tool input/output schemas the agent sees
store.py In-memory storage and its error types
seed.py Fixed corpus: stable ids and timestamps, so evals are reproducible
server.py MCP tool definitions, descriptions, and annotations
cli.py `notes-mcp` entry point
evals/
agent.py Builds the pydantic-ai agent under test + local trace capture
task.py One agent turn against a freshly seeded server — the thing evaluated
evaluators.py Custom pydantic-evals evaluators (tool-not-called, argument-contains)
cases.yaml The dataset itself: cases that probe specific MCP misuse patterns
cases.py Loads cases.yaml — registers the custom evaluators, picks the judge model
__main__.py `python -m evals` — runs the dataset against a live model
tests/
test_store.py Unit tests for the storage layer
test_server.py Protocol-level tests through a real MCP client session
scripts/
lint.sh Ruff + pyright + format check
test.sh Unit + protocol tests (fast, free)
evals.sh Agent-behaviour evals against a live model (slow, costs money)Las descripciones de herramientas son constantes a nivel de módulo en server.py, no docstrings en línea. La redacción de las descripciones es lo que principalmente ajustas cuando una evaluación falla, y tenerla en un solo lugar hace que esos cambios sean legibles.
Primeros pasos
Requiere uv y Python 3.12 (fijado en .python-version).
uv sync # create .venv and install everything
uv run scripts/test.sh # unit + protocol tests
uv run scripts/lint.sh # ruff check, pyright (strict), format check
uv run pre-commit install # optional: run the same checks on commitEjecutar el servidor
uv run notes-mcp # stdio, seeded with the sample notes
uv run notes-mcp --empty # stdio, no notes
uv run notes-mcp --transport streamable-http.mcp.json registra el servidor stdio para este proyecto, por lo que un anfitrión MCP que se cualйтесь desde este directorio — Claude Code, por ejemplo — reconoce automáticamente el servidor notes y puedes trabajarlo a mano.
Para leer la superficie de herramientas que vería un agente — que es de lo que realmente va estas evaluaciones — sin iniciar un agente:
uv run fastmcp list .mcp.json # names, signatures, descriptions
uv run fastmcp list .mcp.json --input-schema # ...with the full JSON schemas
npx @modelcontextprotocol/inspector uv run notes-mcp # MCP Inspector, for clicking aroundAcerca de la versión de
mcp: el servidor está construido sobre la biblioteca independiente FastMCP y no con la copia que antes se incluía dentro del SDK demcpcomomcp.server.fastmcp» — mcp 2.0 eliminó ese módulo. FastMCP decide qué versión demcpnecesita (3.x resuelve mcp 1.x), por lo que [pyproject.toml](pyproject.toml) no tiene una dependenciamcpescrita a mano. El harness de evaluación llega a la misma biblioteca desde el otro lado: el cliente MCP depydantic-aise basa enClient` de FastMCP. Así, las dos mitades de este repositorio concuerdan en la versión por construcción y no por un pin que alguien tenga que mantener. FastMCP 4 es el paso que mueve ambas a mcp 2.x, lo que explica por qué la dependencia está limitada por debajo.
Enfoque de pruebas
Dos capas de pruebas, ejecutadas por scripts/test.sh:
test_store.pycubre la semántica del almacenamiento: singularidad, orden de clasificación, límites y marcas de tiempo. Rápidas, exhaustivas, sin protocolo.test_server.pyacciona el servidor en una sesión de cliente MCP entre procesos (fastmcp.Clientsobre el transporte de memoria de FastMCP), por lo que verifica lo que el agente realmente recibe: la lista de herramientas, esquemas JSON, anotaciones de comportamiento, resultados estructurados y texto de error. El protocolo es real; solo lo son el subproceso y el socket.
Las pruebas asíncronas usan el plugin anyio en lugar de pytest-asyncio, porque el cliente del MCP mantiene un cancel scope abierto durante toda vida de la sesión y anyio ejecute la configuración y desmontaje de los fixtures en la misma tarea.
Un tercer tipo de verificación — las evaluaciones del comportamiento del agente — invoca un modelo real y cuesta dinero, así que no forma parte de la suite de pruebas en absoluto; tiene su propio ejecutor y su propio script, descrito a continuación.
El harness de evaluación
evals/ crea un agente pydantic-ai mínimo — un prompt de sistema genérico de una solo línea, agentes few-shot, sin instrucciones tratadas especialmente — y conecta sus únicas herramientas a través de un servidor Notes MCP en proceso mediante pydantic_ai.mcp.MCPToolset (agent.py). El prompt del sistema es deliberadamente escueto: estas evaluaciones existen para comprobar si los propios nombres, descripciones y esquemas del servidor son suficientes para guiar el comportamiento correcto, y no si la ingeniería de prompts puede cubrir una herramienta débil.
evals/ está en el nivel superior y no bajo src/: es una herramienta de desarrollo para este repositorio, no parte del paquete notes-mcp que cualquiera instalaría.
pydantic_evals ejecuta ese agente sobre un Dataset de Case, cada uno dirigido a uno de los cuatro comportamientos el comienzo de este documento:
Caso | Qué comprueba |
| Al pedirle que elimine “mi nota de la lista de la compra”, el agente llama a |
| Una pregunta cuya respuesta se ha truncado en la vista previa de |
| “Añade crackers a mi lista de la compra” debe leer primero la nota completa; la llamada a |
| Al crear una nota con un título que ya existe, no debe desaparecer en silencio el contenido de la nueva nota ni afirmar que se ha creado un duplicado. |
| Una petición para eliminar una nota inexistente no debe dar lugar a una llamada |
| Un camino feliz para comprobación. |
Los casos están en cases.yaml, non en Python — son datos, por lo que añadir un caso o reformular una rúbrica no toca el código. cases.py es solo el cargador: pasa los evaluadores personalizados a Dataset.from_file (un archivo YAML solo puede nombrar un evaluador que el cargador registro) y establece el modelo juez. La cabecera yaml-language-server de el YAML apunta a cases_schema.json, para que un editor pueda completar y validar los nombres de los evaluadores y sus argumentos; hazlo notar sim registro después de añadir o cambiar un evaluador personalizado:
uv run python -c "from evals.cases import write_json_schema; print(write_json_schema())"Los evaluadores combinan las utilidades incorporadas de pydantic-evals (ToolCorrectness, Contains, MaxToolCalls, LLMJudge para los dos casos con más de una recuperación válida) con dos pequeñas personalizadas en evaluators.py: ToolNotCalled (comprueba que una herramienta no se ha invocado más nunca — no existe ToolNotCalled root check) y ArgumentContains (comprobaciones de subcadenas sobre un argumento de herramienta, para los casos de “el contenido antiguo debe conservarse” donde no puede fijarse la redacción exacta de un LLM con un matching de igualdad ni de subdiccionario).sim Contains. Ambos, como los incorporados, leen las pistas de las llamadas a herramientas que captura la combinación de Agent.instrument_all()y una configuración local delogfire.configure() (send_to_logfire=False); consulta configure_instrumentation()` en agent.py.
__main__.py ejecuta el conjunto de datos, imprime un informe completo y sale non-zero si algo fall y ha sido un error de tarea, un evaluador roto o una aserción fallida. La ejecución así:
uv run scripts/evals.shConfigurar un proveedor
NOTES_MCP_EVAL_MODEL selecciona a la vez el proveedor y el modelo, como cadena números:modelo, y su valor predeterminado es anthropic:claude-haiku-4-5-20251001. Copia example a .env y rellena la sección de cualquiera de estos tres que vayas a usar; scripts/evals.sh carga .env automáticamente (a través de python-dotenv; nunca modifica una variable ya definida en tu shell) y .env está en el .gitignore.
Anthropic API (por defecto) — necesita
ANTHROPIC_API_KEY.OpenAI —
NOTES_MCP_EVAL_MODEL=openai:gpt-5yOPENAI_API_KEY.Amazon Bedrock —
NOTES_MCP_EVAL_MODEL=bedrock:<bedrock-model-id>. La autenticación se realiza por la cadena normal de credenciales deboto3, así que no hay nada específicambre específica que configurar más allá de las variables estándar del SDK de AWS: estableceAWS_PROFILEpara usarlo perfil concretado (AWS_DEFAULT_REGIONtambién si este perfil no define una región; nota que debe serAWS_AWS_DEFAULT_REGION, no noAWS_REGION, que la resolución de región de boto3 no comprueba), o déjalos si está definidos en tu valores/región por defecto.
. Igualmente, no hay código el mismo modelo del proveedor: la cadena de eval_model() se pasa directamente al agente y al LLMJudge, y el infer_model de pydantic-ai resuelve el cliente y las credenciales en función del prefijo de uso que vea.
Available Tools
5 toolscreate_noteA
Create a new note and return it, including the generated id to use in later calls.
Titles must be unique case-insensitively; creating a note whose title already exists is an error, so if the user wants to change an existing note, use update_note.
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | Short, distinctive title. Must be unique. | |
| content | No | Body of the note. May be empty. |
Output Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | Stable identifier for the note. |
| title | Yes | Short human-readable title. |
| content | Yes | Full note body. |
| created_at | Yes | When the note was created (UTC, ISO 8601). |
| updated_at | Yes | When the note was last modified (UTC, ISO 8601); equals `created_at` for a note that has never been updated. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=false, destructiveHint=false), the description reveals the result shape (returns note with generated id), a critical error condition (case-insensitive title collision), and a routing fallback. This adds real behavioral context without contradiction.
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 sentences, the most important return behavior up front, followed by the duplicate-title warning and alternative. Each sentence adds unique operational value; there is no padding.
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 only two parameters, one required, and a full output schema, the description covers everything an agent needs: what the tool does, what it returns, key constraints, and the correct sibling for modification. Nothing essential is missing.
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 both parameters well. The description adds only the case-insensitive uniqueness nuance beyond the schema's existing 'Must be unique' note. That is small extra value, consistent with the baseline 3 for high coverage.
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 a specific verb-resource pair ('Create a new note') and clearly states the distinguishing behavior: returning the generated id for later calls. It also contrasts with update_note by pointing out that duplicate titles are errors, making the tool's role unmistakable among its siblings.
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 explicitly names when not to use this tool ('if the user wants to change an existing note, use update_note') and clearly flags the uniqueness constraint that should silence spurious create attempts. This gives the agent direct decision support.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_noteADestructive
Permanently delete the note with the given id and return what was removed.
This cannot be undone and the note's content is not recoverable, so confirm the id with list_notes or get_note before calling when the user identified the note by description rather than by id.
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes | Id of the note to delete. |
Output Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | Identifier of the note that was deleted. |
| title | Yes | Title of the note that was deleted. |
| deleted | Yes | Always true; a missing note is an error, not a false here. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
While annotations already set destructiveHint=true, the description adds critical context: the deletion is permanent, content is not recoverable, and it returns what was removed. This warns about irreversibility and the need for confirmation beyond the annotation's binary hint.
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 sentences, front-loaded with the core action, and every clause earns its place. The caution and verification guidance are placed logically without 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?
The tool is simple (one parameter, no nested objects, has output schema). The description covers what the tool does, the consequence, and the necessary verification pre-step. Nothing an agent needs to call it correctly is missing.
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 covers the only parameter note_id with a clear description 'Id of the note to delete'. Since schema description coverage is 100%, the baseline is 3. The description refers to the parameter as `id` but adds no new format or semantic detail 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 states a specific verb 'delete' with a specific resource 'note' and the return behavior 'return what was removed'. It distinguishes from siblings like create, get, list, update by naming the destructive action unambiguously.
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 explicit guidance on when to use this tool: confirm the id with list_notes or get_note before calling when the note was identified by description. This directly addresses potential misuse and surfaces the appropriate alternative behavior.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_noteARead-onlyIdempotent
Read one note in full by id.
Use this whenever you need a note's complete body — list_notes previews are cut off after 120 characters. Ids come from list_notes or create_note; this tool does not accept titles.
| Name | Required | Description | Default |
|---|---|---|---|
| note_id | Yes | Id of the note, as returned by `list_notes`. |
Output Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | Stable identifier for the note. |
| title | Yes | Short human-readable title. |
| content | Yes | Full note body. |
| created_at | Yes | When the note was created (UTC, ISO 8601). |
| updated_at | Yes | When the note was last modified (UTC, ISO 8601); equals `created_at` for a note that has never been updated. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context about return behavior (full note body) and explicitly documents a limitation ('does not accept titles'), which goes 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 compact and front-loaded. The first sentence conveys the core purpose, and the second sentence gives the exact context needed to avoid common mistakes 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?
A single-parameter read operation with annotations, a full output schema, and clear sibling differentiation. The description covers all functional details needed to invoke the tool correctly; nothing important is missing.
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% and the parameter description already says 'Id of the note, as returned by list_notes.' The tool description reinforces that note_id comes from list_notes or create_note and that titles won't work, but does not add significant meaning 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 states a specific action: 'Read one note in full by `id`.' It names the resource and the mechanism, and the first sentence identifies the tool as the way to get a note's complete body, distinguishing it from list previews.
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 explicitly says to use this tool whenever a note's complete body is needed, notes that list_notes previews are truncated at 120 characters, and clarifies that IDs come from list_notes or create_note and that titles are not accepted. This provides clear when-to-use and 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.
list_notesARead-onlyIdempotent
List notes, most-recently-updated first, as previews.
Pass query to filter to notes whose title or body contains that text (case-insensitive substring, not fuzzy or semantic — prefer one distinctive word over a whole phrase, and drop the filter entirely to browse everything).
Each entry carries id, title, preview, content_truncated, content_length, created_at, and updated_at. total_count is the number of matches, which exceeds the returned notes when limit cut the page short.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Maximum notes to return. | |
| query | No | Case-insensitive substring filter on title and body. Omit to list every note. |
Output Schema
| Name | Required | Description |
|---|---|---|
| notes | Yes | Matching notes, newest-updated first. Previews only — use `get_note` for full content. |
| total_count | Yes | Number of notes matching the query, which may exceed `len(notes)` when `limit` truncated the page. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
In addition to the annotations (read-only, idempotent, non-destructive), the description adds meaningful behavior: ordering of results, preview semantics, an explicit explanation of the substring behavior, and clarity about total_count exceeding returned results when limit truncates the page. This is exactly the kind of behavioral context the description should 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 three concise paragraphs with a front-loaded summary, a filter guidance section, and a brief output-shape section. Every sentence contributes necessary behavioral or usage detail, and the structure makes it easy for an agent to parse.
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 core operation, ordering, filtering behavior, returned entry fields, total_count semantics, and limit-pagination behavior. Combined with a rich output schema and the read-only annotations, virtually everything an agent needs to invoke this tool correctly and interpret the default behavior is present.
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 already covers both parameters thoroughly: limit defines maximum notes with defaults and bounds, query explains the case-insensitive substring filter and omission. The description restates some of this guidance but does not add significant new meaning for the parameters; its advice about using one distinctive word is minor beyond the schema's coverage.
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 a specific action ('List notes') and a distinctive resource, and also specifies the ordering (most-recently-updated first) and output shape (previews), which precisely distinguishes it from the single-record sibling get_note. It doesn't explicitly call out the sibling, but listing versus retrieving a single note is semantically self-evident.
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 concrete usage guidance for the query filter: it explains that the filter matches case-insensitive substrings, warns that it is not fuzzy or semantic, and counsels preferring one distinctive word over a phrase. It also says to omit the filter to browse everything, but it doesn't explicitly contrast list_notes with get_note for retrieving a single full note.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_noteADestructive
Overwrite the title and/or content of an existing note and return the updated note.
This REPLACES whatever you pass; it does not merge. Omit a field (or pass null) to leave it alone. To add to a note's body without losing the current text, call get_note first and pass the full combined text as content.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | Replacement title, or null to keep the current one. | |
| content | No | Replacement body — overwrites, does not append. Null keeps the current body. | |
| note_id | Yes | Id of the note to change. |
Output Schema
| Name | Required | Description |
|---|---|---|
| id | Yes | Stable identifier for the note. |
| title | Yes | Short human-readable title. |
| content | Yes | Full note body. |
| created_at | Yes | When the note was created (UTC, ISO 8601). |
| updated_at | Yes | When the note was last modified (UTC, ISO 8601); equals `created_at` for a note that has never been updated. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Beyond the annotations (readOnlyHint=false, destructiveHint=true), the description clearly discloses the destructive overwrite behavior: it replaces, does not merge, and explains the semantics of omitting or passing null. It also flags the risk of losing current text and instructs the agent on the safe pattern (get_note first). No contradiction with 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 compact and front-loaded with the core function. Every sentence serves a distinct purpose: state the operation and return value, warn about overwrite semantics, explain null/omit behavior, and provide the append workflow. No redundancy or filler.
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 output schema is present, return values are already documented. The description covers the essential behavioral nuance (no merge, omission semantics) and the critical alternative workflow for append. An agent has everything necessary to call the tool correctly without external 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?
Schema description coverage is 100% and the schema already explains that null keeps the current value for both fields. The description adds the clarification that omitting a field also leaves it alone, which is a small extra, but overall it does not materially extend the schema's parameter 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?
States a specific verb (Overwrite), resource (title and/or content of an existing note), and outcome (return the updated note). This clearly distinguishes the tool from its siblings list_notes, get_note, create_note, and delete_note.
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?
Provides clear context for use: modifying an existing note by overwriting fields. It also gives explicit guidance for the alternative of appending content, telling the agent to call get_note first and pass the full combined text. However, it does not explicitly state exclusions such as 'use create_note for new notes,' though this is implied by 'existing note.'
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.
5 tool updates
v0.1.0- First observed
create_note - First observed
delete_note - First observed
get_note - First observed
list_notes - First observed
update_note
TDQS
Scored across 5 tools
Each tool maps to one distinct resource action (create, get, list, update, delete), with no overlapping operations. The descriptions reinforce boundaries by explaining when to use get_note over list_notes and create_note over update_note.
All tool names follow the same verb_note pattern: create_note, get_note, list_notes, update_note, delete_note. list_notes is pluralized because it returns a collection, but the convention is otherwise uniform and predictable.
Five tools form a tightly scoped set for a notes server: one create tool, one read tool, one list tool, one update tool, and one delete tool. Each tool earns its place and the count is ideal for the domain.
The tool surface covers the full note lifecycle with no dead ends: create, list, read by id, update, and delete. The update tool's replace-only behavior is mitigated by explicit guidance to call get_note first, so agents have a complete workflow.
Maintenance
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Google Keep-style notes app with an MCP server for AI agents to read/write notes.
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Agent-native notes, tasks, dev-docs, vaults, sync & handoffs. MCP + OpenAPI dual surface.
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- -licenseNot gradedqualityNot gradedmaintenanceA simple notes system that allows creating, storing, and accessing text notes through MCP resources and tools, with built-in prompt support for generating summaries of stored notes.-
- FlicenseNot gradedqualityDmaintenanceProvides MCP tools to create and retrieve notes stored in memory.-
- AlicenseAqualityBmaintenanceProvides cross-session persistent memory for coding agents via MCP tools to store, retrieve, and manage notes with hybrid keyword/semantic search and automatic deduplication.8340 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables AI assistants to manage in-memory notes by listing, fetching, and creating notes through MCP tools over stdio.104 npmMIT