Notes MCP
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Notes MCPlist my notes and show the full content of the one about groceries"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Eval Demo
A worked example of using evaluations to verify that an LLM agent can actually use an MCP server effectively — not just that the server's code is correct.
Unit tests answer "does delete_note delete a note?" They cannot answer the questions
that decide whether an MCP server is any good in practice:
Does the agent find the right note when the user describes it in words instead of by id?
Does it notice that a listing preview was truncated, or does it answer from half a note?
Does it realize
update_noteoverwrites, or does it silently destroy the user's content when asked to "add a line to my grocery list"?Does it recover from an error message, or give up?
Those are properties of the tool surface — names, descriptions, schemas, result shapes, error text — and the only way to check them is to run a real agent against the server and grade what it did. That is what this repo is for.
Status
The MCP server, its infrastructure, and the eval harness are all in place.
Related MCP server: MCP Notepad Server
The server under test: Notes MCP
An in-memory notebook. State lives in the server process and is discarded on exit, so every eval run starts from the same known corpus (see seed.py).
Tool | Behaviour hint | What it does |
| write | Creates a note; titles must be unique case-insensitively. |
| read-only | Returns one note's full content, by id. |
| read-only | Lists notes newest-updated first, as truncated previews, with an optional substring |
| destructive | Overwrites the title and/or content of a note. |
| destructive | Permanently removes a note. |
Several design choices exist specifically to give the evals something to catch:
Ids, not titles. Every mutating tool takes a
note_id, so an agent asked to change "my grocery list" must look the id up first. This is where agents commonly guess.Truncated previews.
list_notesreturns only the first 120 characters of each note, flagged withcontent_truncatedandcontent_length. An agent that answers a content question straight from a listing gets it wrong; a good one callsget_note.Replace, not append.
update_noteoverwrites. "Add eggs to my grocery list" is therefore a read-modify-write, and an agent that skips the read destroys data.Errors that teach. Every failure names the offending value and points at the tool that would resolve it, so an agent has a path forward rather than a dead end.
Layout
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)The tool descriptions are module-level constants in server.py rather than inline docstrings. Description wording is the main thing you tune in response to a failing eval, and keeping it in one place makes those diffs readable.
Getting started
Requires uv and Python 3.12 (pinned in .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 commitRunning the server
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 registers the stdio server for this project, so an MCP host
launched from this directory — Claude Code, for instance — picks up the notes server
automatically and you can drive it by hand.
To read the tool surface an agent would see — the thing these evals are really about — without starting an agent at all:
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 aroundOn the
mcpversion: the server is built on the standalone FastMCP library rather than the copy that used to ship inside themcpSDK asmcp.server.fastmcp— mcp 2.0 removed that module. FastMCP owns whichmcpversion it needs (3.x resolves mcp 1.x), so pyproject.toml has no hand-writtenmcpbound. The eval harness arrives at the same library from the other side:pydantic-ai's MCP client is built on FastMCP'sClient. Both halves of this repo therefore agree on a version by construction rather than by a pin someone has to maintain. FastMCP 4 is the step that moves both to mcp 2.x, which is why the dependency is capped below it.
Testing approach
Two pytest layers, run by scripts/test.sh:
test_store.pycovers storage semantics — uniqueness, sort order, limits, timestamps. Fast, exhaustive, no protocol involved.test_server.pydrives the server through an in-process MCP client session (fastmcp.Client, over FastMCP's in-memory transport), so it asserts on what an agent actually receives: the tool list, JSON schemas, behaviour annotations, structured results, and error text. The protocol is real; only the subprocess and socket are not.
Async tests use the anyio pytest plugin rather than pytest-asyncio, because the MCP
client holds a cancel scope open for the life of the session and anyio runs fixture setup
and teardown in the same task.
A third kind of check — the agent-behaviour evals — calls a live model and costs money, so it isn't part of the pytest suite at all; it has its own runner and script, described next.
The eval harness
evals/ builds a minimal pydantic-ai agent — a
generic one-line system prompt, no few-shot examples, no special-cased instructions — and
wires its only tools to an in-process Notes MCP server via pydantic_ai.mcp.MCPToolset
(agent.py). The system prompt is deliberately bare: these evals exist to
check whether the server's own tool names, descriptions, and schemas are enough to guide
correct behaviour, not whether prompt engineering can paper over a weak one.
evals/ lives at the top level rather than under src/: it's dev tooling for this repo,
not part of the notes-mcp package anyone would install.
pydantic_evals runs that agent over a Dataset of
Cases, each targeting one of the four behaviours from the top of this file:
Case | Checks |
| Asked to delete "my grocery list note," the agent calls |
| A question whose answer is truncated out of the |
| "Add crackers to my grocery list" must read the full note first — the |
| Creating a note whose title already exists must not silently drop the new content or claim a duplicate was created. |
| A request to delete a note that doesn't exist must not result in a |
| A sanity-check happy path. |
The cases live in cases.yaml rather than in Python — they're data, so
adding a case or rewording a rubric doesn't touch code. cases.py is only
the loader: it hands the custom evaluators to Dataset.from_file (a YAML file can only
name an evaluator the loader registers) and sets the judge model. The YAML's
yaml-language-server header points at cases_schema.json, so an
editor can complete and validate evaluator names and their arguments; regenerate it after
adding or changing a custom evaluator:
uv run python -c "from evals.cases import write_json_schema; print(write_json_schema())"Evaluators combine pydantic-evals' built-ins (ToolCorrectness, Contains, MaxToolCalls,
LLMJudge for the two cases with more than one valid recovery) with two small custom ones
in evaluators.py: ToolNotCalled (assert a tool was never invoked —
there's no built-in negative check) and ArgumentContains (substring checks on a tool
argument, for "the old content must survive" cases where an LLM's exact wording can't be
pinned down with an equality or subset-dict match). Both, like the built-ins, read
tool-call spans that Agent.instrument_all() plus a local (send_to_logfire=False)
logfire.configure() capture — see configure_instrumentation() in
agent.py.
__main__.py runs the dataset, prints a full report, and exits
non-zero if anything failed — a task error, a crashed evaluator, or a failed assertion.
Run it with:
uv run scripts/evals.shConfiguring a provider
NOTES_MCP_EVAL_MODEL picks both the provider and model, as a pydantic-ai
provider:model string, and defaults to anthropic:claude-haiku-4-5-20251001. Copy
.env.example to .env and fill in the section for whichever of these
three you're using — scripts/evals.sh loads .env automatically (via python-dotenv;
it never overrides a variable already set in your shell) and .env is gitignored:
Anthropic API (default) — needs
ANTHROPIC_API_KEY.OpenAI —
NOTES_MCP_EVAL_MODEL=openai:gpt-5andOPENAI_API_KEY.Amazon Bedrock —
NOTES_MCP_EVAL_MODEL=bedrock:<bedrock-model-id>. Authenticates through boto3's normal credential chain, so there's nothing eval-specific to configure beyond the standard AWS SDK variables: setAWS_PROFILEto use a named profile (AWS_DEFAULT_REGIONtoo, if that profile doesn't already set a region — note it must beAWS_DEFAULT_REGION, notAWS_REGION, which boto3's region resolution doesn't check), or leave both unset to use your default profile/region.
No code branches on the provider — eval_model()'s string is handed straight to both
the agent and LLMJudge, and pydantic-ai's infer_model resolves the right client and
credentials for whichever provider prefix it sees.
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.
TDQS
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.
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