Personal Expense Tracker MCP
Personal Expense Tracker MCP
Was dieses Projekt tut
Personal Expense Tracker MCP ist ein einfacher, lokaler MCP-Server für die Erfassung persönlicher Ausgaben.
Er speichert die Ausgabendaten in einer lokalen CSV-Datei.
Der Server stellt einem MCP-Client fünf gezielte Tools bereit:
eine neue Ausgabe hinzufügen
gespeicherte Ausgaben auflisten
eine Ausgabenübersicht erstellen
eine vorhandene Ausgabe aktualisieren
eine vorhandene Ausgabe löschen
Das Projekt ist bewusst einfach gehalten und soll sich leicht lokal ausführen lassen.
Es sind weder ein Bankkonto noch eine externe API erforderlich.
Related MCP server: Expense Tracker MCP
Voraussetzungen
Bevor du beginnst, stelle sicher, dass Folgendes installiert ist:
Node.js Version 20 oder neuer
npm
Git
Prüfe Node.js.
node --versionPrüfe npm.
npm --versionPrüfe Git.
git --versionInstallation
1 Repository klonen
git clone https://github.com/MohammadYousef-001/Personal-Expenses-Tracker.git2 In den Projektordner wechseln
cd Personal-Expenses-Tracker3 Die Pakete installieren
npm installWarte, bis npm die benötigten Pakete installiert hat.
Datendatei
Die Ausgaben werden lokal gespeichert in:
data/expenses.csvDie CSV-Datei verwendet diese Spalten:
id,amount,category,date,descriptionÄndere die Spaltennamen nicht.
MCP-Server starten
Zum Starten des Servers führe Folgendes aus:
npm run devDer Server nutzt die Standard-Eingabe und -Ausgabe für die MCP-Kommunikation. Er ist ein lokaler Server und benötigt keinen Web-Port.
MCP Inspector starten
MCP Inspector ist der einfachste Weg, die Tools manuell zu testen.
Führe Folgendes aus:
npm run inspectEs sollte sich ein Browserfenster mit MCP Inspector öffnen.
Falls es sich nicht automatisch öffnet, verwende die im Terminal angezeigte URL.
Im Inspector
verbinde dich mit dem Server
öffne den Abschnitt Tools
wähle ein Tool aus
gib die Eingabe ein
klicke auf Execute Tool
prüfe das Ergebnis
Verfügbare Tools
Tool | Was es tut | Wichtigste Eingaben |
| fügt eine neue Ausgabe zur CSV-Datei hinzu | Betrag Kategorie Datum Beschreibung |
| listet gespeicherte Ausgaben auf und kann filtern | Monat Kategorie |
| berechnet die Ausgabensummen für einen Monat | Monat |
| aktualisiert eine vorhandene Ausgabe | ID und mindestens ein Ausgabefeld |
| löscht eine vorhandene Ausgabe | ID |
Tool 1 add_expense
Verwende add_expense, um eine neue Ausgabe zu speichern.
Eingaben
amount
Der ausgegebene Geldbetrag.
Er muss eine positive Zahl sein.
category
Die Ausgabenkategorie.
Beispiele
groceries
transport
bills
shoppingDie Kategorie wird in Kleinbuchstaben gespeichert, damit die Kategorienamen konsistent bleiben.
date
Das Ausgabedatum.
Es muss folgendes Format verwenden:
YYYY-MM-DDBeispiel
2026-08-12Das Datum muss außerdem ein echtes Kalenderdatum sein.
Das ist ungültig:
2026-99-99description
Optionale kurze Information zur Ausgabe.
Beispiel
{
"amount": 25,
"category": "groceries",
"date": "2026-08-12",
"description": "milk and bread"
}Eine erfolgreiche Anfrage speichert die Ausgabe in folgender Datei:
data/expenses.csvTool 2 list_expenses
Verwende list_expenses, um gespeicherte Ausgaben anzusehen.
Ohne Filter
{}Nach Kategorie filtern
{
"category": "groceries"
}Nach Monat filtern
{
"month": "2026-08"
}Beide Filter verwenden
{
"month": "2026-08",
"category": "groceries"
}Der Monat muss folgendes Format verwenden:
YYYY-MMDer Monat muss zwischen 01 und 12 liegen.
Das ist ungültig:
2026-99Tool 3 get_spending_summary
Verwende get_spending_summary, um eine Übersicht deiner Ausgaben zu erhalten.
Beispiel
{
"month": "2026-08"
}Das Ergebnis kann enthalten:
den ausgewählten Monat
den insgesamt ausgegebenen Betrag
die Anzahl der Ausgaben
die Ausgabensumme pro Kategorie
Beispielergebnis
month: 2026-08
total amount: 150
expense count: 4
category totals:
groceries: 80
transport: 30
bills: 40Die tatsächlichen Werte hängen von den Daten in data/expenses.csv ab.
Beispiel-Prompts
Ausgabe hinzufügen
I spent 25 on groceries todayDafür kannst du Folgendes verwenden:
add_expenseAusgaben finden
show my grocery expenses for this monthDafür kannst du Folgendes verwenden:
list_expensesAusgaben überprüfen
how much did I spend this monthDafür kannst du Folgendes verwenden:
get_spending_summaryEingabevalidierung
Das Projekt prüft die Tool-Eingaben, bevor sie verwendet werden.
Beispiele für ungültige Eingaben:
negativer Betrag
Betrag von null
falsches Datumsformat
unmögliches Kalenderdatum
ungültiger Monat
zu lange Kategorie
zu lange Beschreibung
Ungültige Eingaben werden abgewiesen, bevor sie gespeichert werden.
Schemata testen
Führe Folgendes aus:
npm run check:schemaBei einer erfolgreichen Ausführung sollte Folgendes angezeigt werden:
All expense schemas are valid.Fehlerbehebung
Problem 1: Pakete fehlen
Wenn eine Fehlermeldung angezeigt wird, dass ein Paket oder Modul nicht gefunden werden kann, führe Folgendes aus:
npm installVersuche es danach erneut.
npm run devProblem 2: MCP Inspector startet nicht
Stelle sicher, dass die Pakete installiert sind:
npm installDann führe Folgendes aus:
npm run inspectFalls noch ein alter Inspector-Prozess läuft, drücke:
Ctrl + CStarte den Inspector danach erneut.
Problem 3: TypeScript- oder Schema-Fehler
Führe Folgendes aus:
npm run check:schemaWenn alles korrekt ist, sollte Folgendes angezeigt werden:
All expense schemas are valid.Wenn ein Fehler auftritt, prüfe den Dateinamen und die im Terminal angezeigte Zeilennummer. Behebe den Fehler, bevor du den Inspector erneut startest.
Problem 4: Ungültiges Datum
Ein gültiges Datum muss folgendes Format verwenden:
YYYY-MM-DDGültiges Beispiel
2026-08-12Ungültiges Beispiel
2026-99-99Problem 5: Keine Ausgaben werden zurückgegeben
Prüfe:
data/expenses.csvStelle sicher, dass unterhalb der CSV-Kopfzeile Ausgabenzeilen vorhanden sind.
Wenn die Datei nur die Kopfzeile enthält, ist ein leeres Ergebnis normal.
Projektstruktur
Wichtige Projektdateien:
Personal-Expenses-Tracker/
│
├── data/
│ └── expenses.csv
│
├── docs/
│ ├── design.md
│ └── threat-model.md
│
├── examples/
│
├── src/
│ ├── index.ts
│ ├── schemas/
│ ├── tools/
│ └── lib/
│
├── SECURITY.md
├── package.json
└── README.mdSicherheit
Tool-Eingaben werden als nicht vertrauenswürdige Eingaben behandelt.
Das Projekt validiert Eingaben, bevor wichtige Vorgänge ausgeführt werden.
Der Pfad zur Ausgabendatei wird von der Anwendung kontrolliert.
Fehler, die an den MCP-Client zurückgegeben werden, werden kurz gehalten.
Weitere Details zu Fehlern bleiben für Debugging-Zwecke im lokalen Terminal.
Die Ausgaben-Tools benötigen keine externen Netzwerkanfragen.
Weitere Sicherheitsinformationen findest du in:
SECURITY.mdProjektumfang
Dieses Projekt ist ein kleiner, lokaler Ausgaben-Tracker für persönliche Ausgaben.
Enthalten sind nicht:
Bankverbindungen
automatische Bankimporte
Benutzeranmeldung
Cloud-Datenbank
vollständige grafische Anwendung
Finanzprognosen
Das Hauptziel ist es, einfache MCP-Tools für die lokale Ausgabenverfolgung bereitzustellen.
Lizenz
Dieses Projekt verwendet die MIT License.
Siehe LICENSE.
Available Tools
5 toolsadd_expenseC
add a new expense
| Name | Required | Description | Default |
|---|---|---|---|
| date | Yes | The expense date in YYYY-MM-DD format | |
| amount | Yes | The expense amount, up to 1000000 | |
| category | Yes | The normalized expense category | |
| description | No | A short optional description of the expense |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false, so the description does not need to restate that this is a write operation. However, it adds no behavioral context beyond what the name implies—it simply restates 'add a new expense', which is effectively a tautology. There is no mention of side effects, idempotency, validation rules, or what happens on conflict, making it entirely uninformative for behavioral expectations.
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 extremely short (five words), which could be seen as concise, but it is under-specified rather than efficiently structured. It lacks any front-loaded information beyond a bare verb phrase. There is no hierarchy of information; it is merely a label, failing to earn its place by providing any useful detail.
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 four parameters, three of which are required, and no output schema, the description is completely inadequate. It does not mention required fields, expected input format (though schema covers this), possible side effects, or return behavior. An agent has no way to anticipate the tool's behavior or success criteria beyond the raw schema, making the description insufficient for correct invocation in a meaningful 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?
The input schema provides 100% coverage of all four parameters, including descriptions for amount, category, date, and optional description. The description adds no parameter-specific meaning, so the baseline score of 3 applies. While the description doesn't harm parameter understanding, it also doesn't supplement the schema or clarify relationships between parameters.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'add a new expense' conveys a clear verb (add) and resource (expense). It is distinct from sibling tools like update_expense and delete_expense simply by the verb 'add', so an agent can infer its core purpose. However, it does not explicitly distinguish itself from potential overlap or specify any scope (e.g., personal vs. shared), so it stops short of full differentiation.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It does not mention prerequisites, context, or when 'add' is preferred over 'update' or other siblings. An agent is left to infer usage purely from the tool name and schema, which is insufficient for a choice among several expense-related tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
delete_expenseADestructive
delete an existing expense by ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The ID of the expense |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare destructiveHint=true and readOnlyHint=false, indicating a destructive write operation. The description adds no further behavioral context beyond restating the action; it does not mention side effects, irreversibility, or the nature of the response. It does not contradict the annotations, so it meets the baseline but adds little value.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, concise sentence that clearly communicates the action and target. It is front-loaded and contains no extraneous words, achieving appropriate brevity for a simple operation.
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 single-parameter delete operation with no output schema, the description covers the essential purpose and key parameter. It does not describe return values or error conditions, but these are often standard for delete operations and are not critical given the tool's simplicity. The description is complete enough for an agent to call it correctly.
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 fully covers the single parameter 'id' with a description, and the tool description also mentions 'by ID', but adds no additional semantic detail beyond what the schema provides. With 100% schema description coverage, the baseline of 3 applies.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description 'delete an existing expense by ID' clearly states the action (delete) and the resource (expense), and specifies the identifier used. It is unambiguous and easily distinguishes from siblings like add_expense, update_expense, and list_expenses through the verb and resource.
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 implicitly conveys usage by the verb 'delete', but it does not explicitly state when to use it versus the alternatives, nor does it mention any constraints such as required existence or irreversibility. The context is clear enough for an agent to infer usage, but no explicit guidance or exclusions are provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
get_spending_summaryCRead-only
get a spending summary
| Name | Required | Description | Default |
|---|---|---|---|
| month | No | A calendar month in YYYY-MM format |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true and destructiveHint=false, so the safety profile is fully covered and the bar is lower. The description adds only the marginal notion that this returns an aggregated summary, but does not disclose what gets aggregated or scoped. It does not contradict 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 phrase is four words with zero waste and a front-loaded verb, which is efficient. However, the brevity stems more from under-specification than from disciplined trimming, so it does not reach the level of a well-packaged, substance-complete definition.
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 read-only tool with one optional parameter and annotations covering safety, this is a simple tool, but the description omits what the summary contains and what happens when month is omitted. With no output schema and no descriptive context, an agent still cannot predict the return shape or default time scope.
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% — the single month parameter is fully documented with the description 'A calendar month in YYYY-MM format' and a regex pattern. Per the rubric, the baseline is 3 when the schema does the heavy lifting, and the description adds nothing beyond it.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a verb ('get') and a resource ('spending summary'), and it distinguishes itself from the CRUD siblings (add/update/delete/list_expenses) as the aggregation view. However, it essentially restates the tool name ('get_spending_summary' → 'get a spending summary') and adds no new information about what the summary contains, so it sits at minimum viable rather than distinctly helpful.
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 given on when to choose this tool vs its siblings. There is no mention of how a summary differs from list_expenses, when the optional month should be supplied, or what default scope applies if month is omitted. With four sibling tools, this leaves the decision entirely to the agent's inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
list_expensesBRead-only
list expenses with optional filters
| Name | Required | Description | Default |
|---|---|---|---|
| month | No | A calendar month in YYYY-MM format | |
| category | No | The normalized expense category |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The annotations already declare readOnlyHint=true, so the tool is known to be safe. The description adds no behavioral context beyond 'list expenses'—nothing about pagination, sorting, default behavior (e.g., what happens if no filters are provided), or response format. Given annotations cover safety, the description fails to enrich the agent's understanding of how the tool behaves.
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, short sentence with no redundant words or filler. It is efficiently front-loaded with the core action ('list expenses') and the key feature ('optional filters'). Every word 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?
Given the tool's low complexity (2 optional params, no nested objects, no output schema) and strong annotations, the description is minimally adequate. However, it does not explain whether the filters can be combined, what the response looks like, or if there are limits. An agent would have to infer these from the schema and typical conventions. It is complete enough for a basic call but leaves room for ambiguity.
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 both month and category are fully described in the schema. The description's phrase 'optional filters' adds no extra meaning about semantics like combined filtering or format. The baseline is 3 because the schema carries the full load; the description offers no additional insight.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool lists expenses and mentions optional filters, which maps to the parameters. It is distinct from the siblings (add/update/delete/spending summary) since 'list' is unambiguous. However, it does not explicitly name the filter dimensions (month, category) or contrast with the summary tool, so it's specific but not maximally differentiated.
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 on when to use this tool versus alternatives. The description does not mention that get_spending_summary is for aggregated data, nor does it indicate that list_expenses is for raw expense details. The usage context is only implied by the verb 'list' and not explicitly stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
update_expenseB
update an existing expense by ID
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | The ID of the expense | |
| date | No | The expense date in YYYY-MM-DD format | |
| amount | No | The expense amount, up to 1000000 | |
| category | No | The normalized expense category | |
| description | No | A short optional description of the expense |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=false and destructiveHint=false, so the mutation nature is expected. The description adds that it updates an existing expense by ID, implying the target must exist. However, it does not disclose whether it performs a partial update (only provided fields) or a full replacement, nor what happens if the ID is not found. Given the annotations, the additional information is minimal but not contradictory.
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, compact sentence that front-loads the core action. There is no filler or redundant information. It earns its place with zero waste, making it exemplarily 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?
For a mutation tool with 5 parameters, no output schema, and only basic annotations, the description is insufficiently complete. It does not explain the update semantics (partial vs. full), error behavior for missing ID, or what the tool returns. While parameters are covered by the schema, key operational context is absent, leaving an agent to guess.
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% since every parameter (id, date, amount, category, description) has its own description in the schema. The tool description adds no parameter-specific details beyond what the schema already provides. The baseline of 3 is appropriate given the high schema 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 'update' and identifies the resource 'expense' and the key 'by ID'. It clearly distinguishes from sibling tools (add, delete, list, summary) by indicating this modifies an existing record. The purpose is unambiguous.
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 given on when to use this tool versus alternatives, such as using add_expense for new expenses or delete_expense for removal. The description relies on the tool name and context, but does not explicitly state conditions or exclusions.
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
add_expense - First observed
delete_expense - First observed
get_spending_summary - First observed
list_expenses - First observed
update_expense
TDQS
Scored across 5 tools
Each tool has a clearly distinct purpose: add, update, delete, list, and summarize expenses. There is zero overlap, making it easy for an agent to select the right tool for the task.
All tools follow the consistent verb_noun pattern (add_expense, update_expense, delete_expense, list_expenses, get_spending_summary). This creates a predictable and uniform naming convention.
With only 5 tools, the server is tightly scoped for a personal expense tracker. Each tool serves a necessary function without unnecessary bloat, and the count is well within the ideal range.
The toolset covers full CRUD for expenses (add, update, delete, list) plus a summary function. A minor gap is the lack of a direct 'get_expense' by ID, but the list tool with filters can likely retrieve individual expenses, so it's a workable limitation.
Maintenance
Related MCP Connectors
- ManiloOAuthapp.manilo
Log, query, and edit expenses, budgets, and accounts in Manilo from any MCP-compatible AI assistant.
- ManiloOAuthapp.ledgy.api
Log, query, and edit expenses, budgets, and accounts in Manilo (formerly Ledgy) from any MCP-compatible AI assistant.
Track expenses from your AI chat: log, categorize, monthly CSV reports. All data is local.
Log, query, and edit expenses, budgets, and accounts in Ledgy from any MCP-compatible AI assistant.
Related MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables users to track personal expenses through natural language interactions with comprehensive category support and financial summaries. Provides both local and remote MCP server options with SQLite storage for fast expense management operations.-
- FlicenseNot gradedqualityDmaintenanceMCP server for tracking expenses with local SQLite storage. Provides tools to add, list, and summarize expenses by category.-
- FlicenseAqualityCmaintenanceMCP server for managing personal expenses, enabling users to add, list, and summarize expenses by category, with data stored in a local SQLite database.3-
- FlicenseNot gradedqualityBmaintenanceEnables managing and analyzing personal expenses through MCP tools, including adding expense records, listing expenses within date ranges, and summarizing spending by category, with expense categories exposed as an MCP resource.-