A
licenseA
qualityC
maintenanceAn MCP server that helps AI agents reduce token usage by compressing, summarizing, and managing conversation/context data more efficiently.
1
1
MIT
Token Guardian MCP
by KitsuneTech1
Token Guardian MCP
Token Guardian ist ein lokaler, schreibgeschützter MCP-Server, der Token-Verschwendung in Claude Code und Codex erkennt, ohne einen der beiden Clients dümmer zu machen.
Es erfüllt zwei Aufgaben:
Berichtet, welche Agents, Modelle und Sitzungen die meisten Token verarbeiten.
Empfiehlt das günstigste sichere Modell und den Aufwandsgrad für eine bestimmte Aufgabe.
Es enthält auch einen Trockenlauf-Befehl route-task zum Testen des Prompt-First-Routings, bevor der MCP irgendwo registriert wird.
Harte Sicherheitsgrenze
Token Guardian kann keine Modelleinstellungen, Aufwandsgrade, Kontextgrenzen, Kompaktierung, MCP-Registrierung oder laufende Sitzungen ändern. Es besitzt keine Modellanmeldedaten und tätigt keine kostenpflichtigen API-Aufrufe.
Das Nutzungstool ruft ccusage mit --offline und --no-cost auf. Codex-Titel sind optionale Metadaten, die über sqlite3 -readonly gelesen werden. Jedes MCP-Tool ist als schreibgeschützt, nicht destruktiv, idempotent und geschlossene Welt markiert.
Related MCP server: token-pilot
Werkzeuge
token_guardian_usage_snapshot
Liest ein bis dreißig Tage lokaler Nutzung und gibt zurück:
Gesamtsummen nach Agent und Modell
Cache-Lese-, Ausgabe- und Frontier-Modell-Anteile
Größte Sitzungen
Exakte doppelte Codex-Arbeitstitel
Evidenzgestützte schnelle Erfolge
Verarbeitete Token sind eine Arbeitslastdiagnose. Sie sind nicht dasselbe wie ein Abonnementzähler, insbesondere wenn zwischengespeicherte Eingaben dominieren.
token_guardian_recommend_route
Akzeptiert den Client, die Aufgabe, das Risiko und die aktuelle Kontextgröße. Es gibt ein Modell, einen Aufwandsgrad, Gründe und einen optionalen Frontier-Validator zurück.
Die Richtlinie schließt fehl. Sicherheits-, Architektur-, Produktions-, destruktive, risikoreiche, kritische oder mehrdeutige Arbeiten bleiben auf einem Frontier-Modell. Günstige Modelle übernehmen begrenzte mechanische Arbeiten. Ausgewogene Modelle übernehmen normales Codieren und Debuggen, bei Bedarf mit einer Frontier-Überprüfung.
Prompt-First-Routing testen
Erstellen Sie das Projekt und führen Sie dann den Router von einem beliebigen Ordner aus:
node ./dist/route-cli.js --client codex --prompt "Implement a bounded TypeScript parser with tests." --cwd .Der Arbeitsordner muss kein Repository sein. Der Router verwendet hauptsächlich den Prompt. Er überprüft bis zu 256 Namen im aktuellen Ordner auf optionale Projektmarkierungen, ohne Dateiinhalte zu öffnen oder Unterordner zu durchsuchen.
Der Befehl gibt nur eine Empfehlung und einen sitzungsbezogenen Startbefehl aus. Er führt diesen Befehl nicht aus, ändert keine Konfiguration, registriert den MCP nicht und berührt keine aktive Sitzung. Vage Fortsetzungsaufforderungen wie yeah, test that bleiben auf der Frontier-Route, da ihr tatsächlicher Kontext fehlt.
Lokale Anforderungen
Node.js 24 oder neuer
ccusageim PATHsqlite3im PATH für optionale Codex-Thread-Titel
Erstellen und testen
npm install
npm test
npm run typecheck
npm run buildFühren Sie den erstellten Stdio-Server aus:
node dist/index.jsÜberprüfen Sie ihn, ohne ihn in einem Live-Client zu registrieren:
npx @modelcontextprotocol/inspector node dist/index.jsRegistrierungsstatus
Dieser erste Build ist absichtlich nicht bei Claude, Codex oder dem gemeinsamen Kitsune-Gateway registriert. Die Registrierung und ein etwaiger Client-Neustart sind eine separate Änderung, nachdem der isolierte Server akzeptiert wurde.
Lizenz
MIT. Siehe LICENSE.
Available Tools
2 toolstoken_guardian_recommend_routeRecommend Model RouteARead-onlyIdempotent
Recommend a conservative Claude, Codex, or local model and effort for one task. Returns advice only and never switches the active client.
| Name | Required | Description | Default |
|---|---|---|---|
| risk | No | normal | |
| client | Yes | Client that will perform the task. | |
| task_kind | No | Explicit task class. Omit only when genuinely unknown. | |
| task_summary | Yes | Short description of the actual work. | |
| current_model | No | ||
| context_tokens | No | ||
| current_effort | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| tier | Yes | |
| model | Yes | |
| client | Yes | |
| effort | Yes | |
| actions | Yes | |
| reasons | Yes | |
| validator | No | |
| confidence | Yes | |
| advisoryOnly | Yes |
TDQS
A3.6/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context: 'Returns advice only and never switches the active client', confirming the tool's side-effect-free nature. No contradiction with annotations. The description could mention error handling or latency but is sufficient given the strong annotation coverage.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loaded with the key action, and contains no filler. Every word adds value: 'conservative' qualifies the recommendation, 'one task' sets scope, and 'never switches' clarifies behavior. Ideal conciseness.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's complexity (7 parameters, output schema exists, good annotations), the description covers the core workflow but leaves out context like the meaning of 'conservative', 'effort', or how the recommendation relates to the input parameters. The output schema might fill gaps, but the description could be more helpful for understanding the tool's role in a multi-step workflow.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 43% (3 of 7 parameters have descriptions). The description adds no parameter-specific information; it only states the overall purpose. Parameters like 'risk', 'current_effort', and 'context_tokens' remain undocumented, and the description does not compensate for the low coverage. The agent must rely on the incomplete schema, which is insufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb 'recommend', the resource ('model and effort'), and the scope ('for one task'). It also distinguishes from the sibling tool 'token_guardian_usage_snapshot' by emphasizing that it returns advice only and does not change state, making the purpose specific and unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no explicit guidance on when to use this tool versus the sibling 'token_guardian_usage_snapshot'. It states that it returns advice only, implying safe usage, but does not mention when to choose recommendations over usage snapshots or any prerequisites or exclusions. This leaves the agent without clear direction on invocation context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
token_guardian_usage_snapshotToken Usage SnapshotARead-onlyIdempotent
Read local Claude Code and Codex usage, identify large contexts and expensive routing, and return evidence-backed quick wins. Never changes settings or sessions.
| Name | Required | Description | Default |
|---|---|---|---|
| days | No | ||
| agent | No | all | |
| top_sessions | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| caveat | Yes | |
| totals | Yes | |
| window | Yes | |
| byAgent | Yes | |
| byModel | Yes | |
| metrics | Yes | |
| quickWins | Yes | |
| topSessions | Yes | |
| duplicateCodexWork | Yes |
TDQS
A4/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint as safe. The description reinforces this by stating 'Never changes settings or sessions.' It adds value by describing what the tool does with the data (identify, return quick wins), which goes beyond the annotations' safety profile.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Two efficient sentences that pack in the tool's purpose, outcomes, and safety guarantee. Every word serves a purpose with no redundancy or filler.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description is adequate for a read-only snapshot tool with an output schema and sensible defaults, but it fails to mention the tunable parameters (days, agent, top_sessions) that control the snapshot scope. The sibling tool is not contrasted, leaving the agent to infer the relationship.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
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, but it does not mention the three parameters (days, agent, top_sessions) or their roles. The parameter names are somewhat self-explanatory, but the description adds no additional meaning beyond the schema's type and default constraints.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool reads usage data, identifies large contexts and expensive routing, and returns quick wins. It uses a specific verb ('Read') and resource ('local Claude Code and Codex usage'), and distinguishes from the sibling tool 'token_guardian_recommend_route' by focusing on analysis rather than recommendations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description explicitly states it never changes settings or sessions, implying it is safe for inspection. It contrasts with the sibling by focusing on analysis and quick wins, but does not provide explicit 'when to use vs when not to use' guidance. The context is clear enough for an agent to infer appropriate usage.
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.
2 tool updates
v0.1.0- First observed
token_guardian_recommend_route - First observed
token_guardian_usage_snapshot
TDQS
A3.7/5.0
Scored across 2 tools
Disambiguation5/5
Each tool serves a distinct, non-overlapping purpose: one provides a usage snapshot with improvement suggestions, the other recommends a routing choice for a task. There is no ambiguity between them.
Naming Consistency5/5
Both tool names follow a consistent 'token_guardian_<verb>_<noun>' pattern using snake_case, with descriptive verbs ('usage_snapshot', 'recommend_route') that clearly indicate their function.
Tool Count3/5
With only 2 tools, the server feels minimal for a domain that might benefit from additional diagnostics (e.g., cost breakdown, model listing) or configuration advice. The count is on the low end of acceptable for a focused utility.
Completeness2/5
The server explicitly avoids any action tools, being read-only. It covers snapshot analysis and routing recommendations but lacks tools for detailed queries (e.g., by time period, by model) or applying any changes, leaving notable gaps for hands-on usage optimization.
Maintenance
ActivityStale
ResponsivenessNo issues
Related MCP Connectors
LLM cost optimization gateway, repo token-waste auditor & model arbitrage MCP server.
LLM cost optimization gateway, repo token-waste auditor & model arbitrage MCP server.
Hosted MCP server for LLM cost estimation, model comparison, and budget-aware routing.
MCP server for building and testing AI agents with multi-model experimentation and insights.
Related MCP Servers
- AlicenseAqualityAmaintenanceMCP server that reduces token consumption in AI coding assistants by up to 90% via structural reads, PreToolUse hooks, and tp-\* subagents.25162 npm5MIT
- AlicenseNot gradedqualityDmaintenanceMCP server that analyzes AI agent session logs to find token waste and optimization opportunities.8 npmMIT
- AlicenseNot gradedqualityDmaintenanceAn MCP server that reduces token usage by lazily loading skills and tools only when needed, and routing repetitive subtasks to ML backends instead of the LLM.Apache 2.0