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llmprobe

Name: llmprobe
Author: Jwrede

by Jwrede

Overview Schema Source Related Servers Discussions Admin

Local

llmprobe

Untersuchen Sie LLM-API-Endpunkte. Messen Sie TTFT, Latenz und Durchsatz. Einzelne Binärdatei, keine SDKs.

License: MIT llmprobe MCP server

llmprobe ist ein CLI-Tool, das LLM-API-Endpunkte untersucht und die Metriken misst, die für die Zuverlässigkeit in der Produktion wichtig sind: Zeit bis zum ersten Token (TTFT), Gesamtlatenz, Generierungsdurchsatz (Token/Sek.) und Fehlerraten.

Verwenden Sie es als einmalige Gesundheitsprüfung, als kontinuierliche Überwachung oder als CI-Gate, das Bereitstellungen blockiert, wenn Ihr LLM-Anbieter beeinträchtigt ist.

demo

Schnellstart

Laden Sie eine vorgefertigte Binärdatei von der neuesten Version herunter (Linux, macOS, Windows; amd64 und arm64).

Oder installieren Sie es aus dem Quellcode:

go install github.com/Jwrede/llmprobe@latest

Erstellen Sie eine probes.yml (oder kopieren Sie das beigefügte Beispiel):

providers:
  - name: openai
    api_key: ${OPENAI_API_KEY}
    models:
      - name: gpt-4o
        thresholds:
          max_ttft: 2s
      - name: gpt-4o-mini
        thresholds:
          max_ttft: 500ms

  - name: anthropic
    api_key: ${ANTHROPIC_API_KEY}
    models:
      - name: claude-sonnet-4-20250514
        thresholds:
          max_ttft: 1s

Führen Sie eine Untersuchung durch:

$ llmprobe probe

Provider   Model                    Status    TTFT    Latency  Tok/s  Tokens  Error
--------   -----                    ------    ----    -------  -----  ------  -----
openai     gpt-4o                   healthy   312ms   2100ms   68.4   42
openai     gpt-4o-mini              healthy   98ms    814ms    112.3  56
anthropic  claude-sonnet-4-20250514 healthy   420ms   2831ms   52.1   38
azure      gpt-4o                   healthy   289ms   1950ms   71.2   44
bedrock    anthropic.claude-3-5...  degraded  1820ms  4510ms   28.1   38

4 healthy, 1 degraded, 0 errors

Related MCP server: LLM API Benchmark MCP Server

Was es misst

Metrik	Bedeutung
TTFT	Zeit vom Senden der Anfrage bis zum ersten Inhalts-Token. Dies ist das, was Benutzer als "Verzögerung" empfinden, bevor die Antwort gestreamt wird.
Latenz	Gesamtzeit von der Anfrage bis zum Schließen des Streams.
Tok/s	Generierungsdurchsatz: pro Sekunde produzierte Token nach dem ersten Token. Berechnet als `token_count / (latency - ttft)`.
Token	Gesamtzahl der Ausgabe-Token. Bevorzugt Nutzungsmetadaten des Anbieters, falls verfügbar, andernfalls wird auf SSE-Ereigniszählung zurückgegriffen.
Status	`healthy`, wenn alle Schwellenwerte eingehalten werden, `degraded`, wenn ein Schwellenwert überschritten wird, `error`, wenn die Anfrage fehlgeschlagen ist.

Befehle

`llmprobe probe`

Einmalige Gesundheitsprüfung. Untersucht alle konfigurierten Endpunkte und gibt die Ergebnisse aus.

llmprobe probe                        # table output
llmprobe probe -f json                # JSON output
llmprobe probe --fail-on degraded     # exit 1 if any endpoint is degraded
llmprobe probe -c custom-config.yml   # custom config path

Exit-Codes für CI:

`--fail-on`	Exit 0	Exit 1
`error` (Standard)	healthy oder degraded	jeder Fehler
`degraded`	nur healthy	degraded oder error
`none`	immer	niemals

`llmprobe watch`

Kontinuierliche Überwachung. Untersucht alle Endpunkte in einem Intervall und gibt pro Iteration eine Zusammenfassungszeile aus.

llmprobe watch                          # default 60s interval
llmprobe watch --interval 30s           # custom interval
llmprobe watch --tui                    # live terminal dashboard with TTFT chart
llmprobe watch --tui --load data.jsonl  # load historical data into the dashboard
llmprobe watch -f json                  # JSONL output (one line per result)

Das Flag --tui startet ein Live-Terminal-Dashboard mit einem TTFT-Diagramm, einer Farblegende und einer Statistik-Tabelle. Verwenden Sie --load, um historische JSONL-Daten zu importieren (von llmprobe watch -f json > data.jsonl).

llmprobe

$ llmprobe watch --interval 30s

Watching 4 endpoints every 30s (Ctrl+C to stop)

[14:01:02] All 4 endpoints healthy.
[14:01:32] All 4 endpoints healthy.
[14:02:02] 3 healthy, 1 degraded, 0 errors. DEGRADED: openai/gpt-4o (TTFT 1820ms)
[14:02:32] All 4 endpoints healthy.

CI-Integration

Verwenden Sie llmprobe probe als Pre-Deploy-Gate:

# .github/workflows/deploy.yml
- name: Check LLM providers
  env:
    OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
    ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
  run: |
    go install github.com/Jwrede/llmprobe@latest
    llmprobe probe --fail-on degraded

Dies blockiert die Bereitstellung, wenn ein LLM-Anbieter derzeit eine beeinträchtigte Leistung aufweist.

MCP-Server

llmprobe MCP server

llmprobe enthält einen integrierten Model Context Protocol-Server, der es Claude Code und anderen MCP-Hosts ermöglicht, die LLM-API-Gesundheit direkt aus einem Agenten-Workflow zu überprüfen.

Ausführen des Servers

llmprobe mcp

Dies startet den MCP-Server über stdio.

Registrierung bei Claude Code

claude mcp add --transport stdio llmprobe -- llmprobe mcp

Nach der Registrierung kann Claude Code während jeder Konversation llmprobe-Tools aufrufen.

Verfügbare Tools

Tool	Beschreibung
`probe_all`	Untersucht alle konfigurierten Endpunkte aus `probes.yml`. Gibt TTFT, Latenz, Durchsatz und Gesundheitsstatus für jedes Modell zurück. Akzeptiert einen optionalen `config`-Parameter für einen benutzerdefinierten Konfigurationspfad.
`probe_model`	Untersucht ein einzelnes Modell ohne Konfigurationsdatei. Erfordert `provider` (openai, anthropic, google, azure, bedrock), `model` (die Modellkennung) und `api_key_env` (Umgebungsvariable, die den API-Schlüssel enthält).
`list_providers`	Listet alle Anbieter und Modelle in der Konfigurationsdatei mit ihren Schwellenwerten auf. Verwenden Sie dies, um verfügbare Modelle vor der Untersuchung zu entdecken.
`get_config`	Gibt die vollständig geparste Konfiguration zurück, einschließlich Standardwerten, Anbietern, Modellen und Schwellenwerten.

Beispielanwendungsfall: Ein Agent ruft list_providers auf, um zu sehen, welche Modelle konfiguriert sind, und dann probe_all, um zu überprüfen, ob sie gesund sind, bevor Änderungen bereitgestellt werden.

Konfiguration

defaults:
  prompt: "Hello"                                # probe prompt
  max_tokens: 20                                 # max output tokens
  timeout: 30s                                   # per-probe timeout
  concurrency: 5                                 # max parallel probes

providers:
  - name: openai                    # openai, anthropic, google, azure, bedrock
    api_key: ${OPENAI_API_KEY}      # env var expansion
    base_url: https://custom.api    # optional, override endpoint
    models:
      - name: gpt-4o
        prompt: "Say hello."        # override default prompt
        max_tokens: 10              # override default max_tokens
        thresholds:
          max_ttft: 2s              # alert if TTFT exceeds this
          max_latency: 10s          # alert if total latency exceeds this
          min_tokens_per_sec: 20    # alert if throughput drops below this

  - name: azure
    api_key: ${AZURE_OPENAI_API_KEY}
    base_url: https://your-resource.openai.azure.com
    api_version: "2024-10-21"       # optional, defaults to 2024-10-21
    models:
      - name: gpt-4o               # deployment name

  - name: bedrock
    access_key: ${AWS_ACCESS_KEY_ID}
    secret_key: ${AWS_SECRET_ACCESS_KEY}
    region: us-east-1
    models:
      - name: anthropic.claude-3-5-sonnet-20241022-v2:0

API-Schlüssel und AWS-Anmeldeinformationen unterstützen die ${ENV_VAR}-Syntax. Nur Anmeldeinformationsfelder werden erweitert, sodass Verweise auf Umgebungsvariablen in Prompts oder Modellnamen unverändert bleiben.

OpenAI-kompatible Anbieter

Viele Anbieter (Groq, Together AI, Fireworks, DeepSeek, Mistral, OpenRouter, Ollama, vLLM) stellen eine OpenAI-kompatible API bereit. Diese funktionieren sofort durch Festlegen von base_url:

providers:
  # Groq
  - name: openai
    api_key: ${GROQ_API_KEY}
    base_url: https://api.groq.com/openai
    models:
      - name: llama-3.3-70b-versatile

  # DeepSeek
  - name: openai
    api_key: ${DEEPSEEK_API_KEY}
    base_url: https://api.deepseek.com
    models:
      - name: deepseek-chat

  # Together AI
  - name: openai
    api_key: ${TOGETHER_API_KEY}
    base_url: https://api.together.xyz
    models:
      - name: meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo

  # Local Ollama
  - name: openai
    api_key: unused
    base_url: http://localhost:11434/v1
    models:
      - name: llama3.2

Architektur

probes.yml
  -> Config loader (YAML + env var expansion)
    -> Probe engine (concurrent goroutines per provider/model)
      -> Provider clients (raw HTTP + SSE parsing, no SDKs)
        -> Results (TTFT, latency, tokens/sec, status)
          -> Output (table, JSON, JSONL)

Jeder Anbieter-Client ist ein schlanker HTTP-Wrapper, der eine Streaming-Anfrage sendet und die Antwort parst. Es werden keine LLM-SDKs importiert. Der SSE-Parser verarbeitet sowohl reine Datenereignisse (OpenAI, Google) als auch benannte Ereignisse (Anthropic). Der Bedrock-Client implementiert SigV4-Signierung und AWS-Binär-Event-Stream-Parsing von Grund auf.

TTFT wird von dem Moment an gemessen, in dem die HTTP-Anfrage gesendet wird, bis zum ersten Ereignis, das tatsächlichen Inhaltstext enthält (keine Rollenzuweisungen oder Metadaten).

Anbieter

Anbieter	Endpunkt	Authentifizierung	Streaming-Format
OpenAI	`/v1/chat/completions`	`Authorization: Bearer`	SSE, `[DONE]` Sentinel
Anthropic	`/v1/messages`	`x-api-key` Header	benanntes Ereignis SSE
Google	`/v1beta/models/{model}:streamGenerateContent?alt=sse`	`key` Abfrageparameter	SSE
Azure OpenAI	`/openai/deployments/{model}/chat/completions`	`api-key` Header	SSE, `[DONE]` Sentinel
AWS Bedrock	`/model/{model}/converse-stream`	SigV4	AWS Binär-Event-Stream
OpenAI-kompatibel	`/v1/chat/completions` (benutzerdefinierte base_url)	`Authorization: Bearer`	SSE

OpenAI-kompatibel umfasst: Groq, Together AI, Fireworks, DeepSeek, Mistral, OpenRouter, Ollama, vLLM und jeden Endpunkt, der die OpenAI-Chat-Completions-API spricht.

Live-Benchmark

llm-bench verwendet llmprobe, um einen kontinuierlichen öffentlichen Benchmark der wichtigsten LLM-APIs auszuführen. Die Ergebnisse werden als offener JSONL-Datensatz und als Live-Terminal-Dashboard unter bench.jonathanwrede.de veröffentlicht.

Roadmap

Baseline-Tracking: Speichern rollierender Perzentile, Warnung, wenn die aktuelle Untersuchung die Nx-Baseline überschreitet
OpenTelemetry-Metrikexport zur Integration mit Grafana/Datadog
Prometheus /metrics-Endpunkt
Validierung strukturierter Ausgaben: Überprüfung, ob JSON-Modus-Antworten korrekt geparst werden

Lizenz

MIT

Available Tools

4 tools

get_configA

Return the full parsed configuration including defaults, providers, models, and thresholds. Useful for understanding the current probe setup or debugging configuration issues.

ParametersJSON Schema

Name	Required	Description	Default
`config`	No	path to probes.yml config file

TDQS

A4.1/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Describes return contents but does not mention side effects, auth requirements, or rate limits. No annotations exist to supplement.

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, no redundancy, front-loaded with purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Adequately covers purpose, return content, and common use cases for a simple tool with one optional parameter and no output schema.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers the single parameter with description. Description adds no extra meaning beyond 'full parsed configuration'; baseline 3 due to high coverage.

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?

Clearly states it returns the full parsed configuration, listing included elements (defaults, providers, models, thresholds). Distinct from siblings like list_providers or probe_all.

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?

Indicates usefulness for understanding setup or debugging, implying context. Lacks explicit when-not-to-use or comparison to siblings.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

list_providersA

List all providers and models defined in the config file. Returns provider names, model identifiers, and any configured thresholds. Use this to discover what models are available before probing.

ParametersJSON Schema

Name	Required	Description	Default
`config`	No	path to probes.yml config file

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Implicitly a read operation, but with no annotations, the description should explicitly state it is read-only or disclose any side effects. It lacks explicit non-destructive guarantee.

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 concise sentences: first states what it does, second tells when to use it. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a simple single-parameter tool with no output schema, the description is sufficiently complete, covering purpose, returns, and usage context. Minor gap in behavioral transparency.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a single parameter described. The description adds no additional meaning beyond what the schema provides.

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 it lists providers and models from a config file, specifying the exact information returned (names, identifiers, thresholds). This distinguishes it from sibling tools like 'probe_all' or 'get_config'.

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?

Explicitly advises using it 'before probing', providing clear usage context. However, it does not contrast with siblings or specify when not to use it.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

probe_allA

Probe all configured LLM API endpoints. Returns TTFT (ms), total latency (ms), throughput (tokens/sec), and health status for every model in the config file.

ParametersJSON Schema

Name	Required	Description	Default
`config`	No	path to probes.yml config file

TDQS

A3.9/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. It discloses return values and that it uses a config file, but does not mention side effects, error handling, or whether it's read-only. Adequate but not comprehensive.

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?

Single sentence that is concise, front-loaded, and contains no filler. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description adequately explains the return values. The single optional parameter is well-described. Sibling tool context implies complementarity with 'probe_model'. Complete for a probing tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with a clear description for the 'config' parameter. The description adds context by explaining that the config file determines which endpoints are probed, enhancing the schema's meaning.

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 action ('Probe all configured LLM API endpoints') and specifies the exact metrics returned (TTFT, latency, throughput, health status). It distinguishes from sibling 'probe_model' which likely targets a single model.

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?

No guidance on when to use this tool versus alternatives like 'probe_model' or 'get_config'. The description does not specify prerequisites or scenarios where this tool is appropriate.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

probe_modelA

Probe a single LLM model by provider and model name. Use this for ad-hoc checks without a config file. Returns TTFT (ms), total latency (ms), throughput (tokens/sec), and health status.

ParametersJSON Schema

Name	Required	Description
`provider`	Yes	provider name (openai, anthropic, google, azure, bedrock)
`model`	Yes	model identifier (e.g. gpt-4o, claude-sonnet-4-20250514)
`api_key_env`	Yes	environment variable name containing the API key
`base_url`	No	optional base URL for OpenAI-compatible endpoints (e.g. http://localhost:8000)
`label`	No	optional display name for the endpoint (e.g. vllm-local)

TDQS

A4/5.0

Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses return values (TTFT, latency, throughput, health status) and states it probes a model, but fails to mention side effects (e.g., real API call) or safety properties (read-only vs. destructive). This is adequate but not comprehensive.

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 sentences, each carrying essential information: first sentence states purpose and parameters, second sentence clarifies usage context and return values. No redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 5-parameter tool with no output schema, the description adequately covers return values and usage context. It could mention that the tool makes a live API call, but overall completeness is high for a simple diagnostic tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so baseline is 3. The description does not add parameter-level details beyond what the schema already provides; it only mentions 'provider and model name' generically.

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 uses a specific verb ('Probe') and explicitly states the resource ('single LLM model by provider and model name'). It also distinguishes from siblings (probe_all, list_providers) by noting ad-hoc single-model use without a config file.

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 clearly recommends use for 'ad-hoc checks without a config file', implying when to use this tool. Sibling names (probe_all, list_providers, get_config) provide contrast, but no explicit exclusions or when-not-to-use guidance are given.

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.

6 tool updatesv1.4.0May 29, 2026
- Removedcheck_model
- Addedget_config
- Addedlist_providers
- Removedprobe
- Addedprobe_all
- Addedprobe_model
2 tool updatesv0.1.0May 7, 2026
- First observedcheck_model
- First observedprobe

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.3/5.0

Scored 2026-05-29 21:15 across 4 tools

Disambiguation5/5

Each tool has a distinct purpose: configuration retrieval, provider listing, bulk probing, and single model probing. No overlap.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (get_config, list_providers, probe_all, probe_model) with appropriate verbs.

Tool Count5/5

4 tools is well-scoped for the LLM probing domain, covering necessary operations without bloat.

Completeness5/5

The tool set covers all essential probe operations: viewing config, listing providers, probing all endpoints, and probing a single endpoint, with no obvious gaps.

Maintenance

ActivityInactive

ResponsivenessUnresponsive

Resources

GitHub Repository

Need Help?

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llmprobe

Schnellstart

Was es misst

Befehle

llmprobe probe

llmprobe watch

CI-Integration

MCP-Server

Ausführen des Servers

Registrierung bei Claude Code

Verfügbare Tools

Konfiguration

OpenAI-kompatible Anbieter

Architektur

Anbieter

Live-Benchmark

Roadmap

Lizenz

Available Tools

Tool Schema Changelog

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

qa-probeofficial

`llmprobe probe`

`llmprobe watch`