Skip to main content
Glama

Jentic MCP Plugin

Schmiedeabzeichen

Warum Jentic MCP verwenden?

Jentic MCP ermöglicht Entwicklern die schnelle Erkennung und Integration externer APIs und Workflows – ohne dass API-spezifischer Code geschrieben oder gepflegt werden muss. Durch die Nutzung des MCP-Protokolls und der agentenbasierten Laufzeitumgebung von Jentic können Entwickler:

  • Suchen Sie nach APIs und Workflows nach Funktion, nicht nach Anbieter oder Implementierung.

  • Generieren Sie sofort Integrationscodebeispiele, die unabhängig von spezifischen API-Details sind.

  • Vermeiden Sie Standardtexte und reduzieren Sie den Wartungsaufwand, indem Sie auf eine standardisierte, deklarative Integration setzen.

  • Konzentrieren Sie sich auf die Erstellung von Funktionen, während Jentic MCP die Komplexität der API-Interaktion übernimmt.

Related MCP server: agent-zone-mcp

API-Tools

Das Jentic MCP Plugin bietet die folgenden Tools:

  1. search_apis : Suche nach APIs im Jentic-Verzeichnis, die bestimmten Funktionsanforderungen entsprechen

  2. load_execution_info : Ruft detaillierte Spezifikationen für APIs und Operationen aus dem Jentic-Verzeichnis ab. Dies beinhaltet Authentifizierungsinformationen, die Sie möglicherweise in Ihrer mcpServers.jentic.env -Konfiguration angeben müssen.

  3. execute : Führen Sie eine bestimmte API- oder Workflow-Operation aus.

Erste Schritte

Die empfohlene Methode ist, den Server direkt aus dem GitHub-Repository mit uvx auszuführen. Sie müssen uv zuerst mit folgendem Befehl installieren:

brew install uv oder pip install uv

Holen Sie sich Ihre Jentic UUID

Um das Jentic SDK nutzen zu können, benötigen Sie zunächst eine Jentic-UUID. Am einfachsten geht das über die Jentic-CLI. Optional können Sie eine E-Mail-Adresse angeben, um höhere Ratenlimits und frühzeitigen Zugriff auf neue Funktionen zu erhalten.

pip install jentic
jentic register --email '<your_email>'

Dadurch werden Ihre UUID und ein Exportbefehl zum Festlegen in Ihrer Umgebung gedruckt:

export JENTIC_UUID=<your-jentic-uuid>

Legen Sie die Jentic-UUID in Ihrer MCP-Clientkonfiguration wie unten gezeigt fest.

Der Speicherort der Konfigurationsdatei hängt vom verwendeten Client und Ihrem Betriebssystem ab. Einige gängige Beispiele:

  • Windsurf : ~/.codeium/windsurf/mcp_config.json

  • Claude Desktop : ~/Library/Application Support/Claude/claude_desktop_config.json

  • Claude-Code : ~/.claude.json

  • Cursor : ~/cursor/.mcp.json

Informationen zum Hinzufügen von MCP-Servern finden Sie bei anderen Clients in der Dokumentation Ihres Clients.

{
    "mcpServers": {
        "jentic": {
            "command": "uvx",
            "args": [
                "--from",
                "git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp",
                "mcp"
            ],
            "env": {
                "JENTIC_UUID": "<your-jentic-uuid>"
            }
        }
    }
}

Hinweis: Nach dem Speichern der Konfigurationsdatei müssen Sie möglicherweise die Clientanwendung (Windsurf, Claude Desktop) neu starten, damit die Änderungen wirksam werden.

Verwendung des MCP-Tools

Sobald der MCP-Server läuft, können Sie die MCP-Tools in Ihrem LLM-Agenten problemlos verwenden, um APIs und Workflows zu erkennen und auszuführen.

  1. search_apis : Suche nach APIs im Jentic-Verzeichnis, die bestimmten Funktionsanforderungen entsprechen

  2. load_execution_info : Ruft detaillierte Spezifikationen für APIs und Operationen aus dem Jentic-Verzeichnis ab. Dies beinhaltet Authentifizierungsinformationen, die Sie möglicherweise in Ihrer mcpServers.jentic.env -Konfiguration angeben müssen.

  3. execute : Führen Sie eine bestimmte API- oder Workflow-Operation aus.

Umgebungsvariablen

Wenn Sie eine API verwenden, die eine Authentifizierung erfordert, beschreibt das Tool load_execution_info die erforderlichen Umgebungsvariablen. Sie können Umgebungsvariablen über die Kommandozeile in Windsurf einrichten. In einigen Clients wie Claude Desktop müssen Sie sie jedoch Ihrer MCP-Konfiguration hinzufügen:

{
    "mcpServers": {
        "jentic": {
            "command": "uvx",
            "args": [
                "--from",
                "git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp",
                "mcp"
            ],
            "env": {
                "DISCORD_BOTTOKEN=": "YOUR BOT TOKEN"
            }
        }
    }
}

Alternative (Verwendung des lokalen Pfads für die Entwicklung):

Verwenden Sie dies, wenn Sie das MCP-Plugin lokal aktiv entwickeln. Ersetzen Sie /path/to/your/project/mcp durch den absoluten Pfad zu Ihrem Projektverzeichnis.

{
    "mcpServers": {
        "jentic": { 
            "command": "uvx",
            "args": [
                "--from",
                "/path/to/your/project/mcp",
                "mcp" 
            ]
        }
    }
}

Optional: Fügen Sie Ihrer Datei mcp_config.json eine Umgebungsvariable JENTIC_API_URL hinzu, um auf eine bestimmte Jentic-API zu verweisen (funktioniert mit beiden Methoden):

{
    "mcpServers": {
        "jentic": {
            "command": "uvx",
            "args": [
                "--from",
                "git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp",
                "mcp"
            ],
            "env": {
                "JENTIC_API_URL": "https://your-jentic-api.url/"
            }
        }
    }
}

Starten Sie Windsurf nach der Konfiguration neu und die Jentic MCP-Tools sind verfügbar.

Sie können die vom lokal ausgeführten MCP-Server generierten Protokolle verfolgen, indem Sie Folgendes ausführen:

tail /path/to/mcp/jentic_ark2_mcp.log

Installation

Installation über Smithery

So installieren Sie das Jentic-Plugin für Claude Desktop automatisch über Smithery :

npx -y @smithery/cli install @jentic/jentic-tools --client claude

Manuelle Installation

Stellen Sie sicher, dass Sie pdm installiert haben ( pipx install pdm ).

So installieren Sie das Paket und seine Abhängigkeiten für die Entwicklung:

# From the project root directory (e.g., /Users/kc/c/sdk/mcp)
pdm install -G dev

Ausführen des MCP-Servers

Das Jentic MCP-Plugin ist für die Ausführung mit uvx konzipiert, das die Einrichtung und Ausführung der Umgebung übernimmt.

Standardmodus (Stdio)

Führen Sie das MCP-Plugin direkt mit uvx aus und geben Sie das Projektverzeichnis als Quelle mit --from und dem mcp -Skript an:

Vom lokalen Pfad (Entwicklung):

# Use --from with the project directory and specify the 'mcp' script
uvx --from /path/to/your/project/mcp mcp

# Or, if running from within the project directory:
uvx --from . mcp

Aus dem Remote-Repository (für die allgemeine Verwendung empfohlen):

uvx --from \
  git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
  mcp

Dabei wird automatisch der im Rückruf des mcp Skripts definierte Standardbefehl serve --transport stdio verwendet.

HTTP-Modus

So führen Sie den Server im HTTP-Modus aus (z. B. zum Testen mit claude-cli ):

Vom lokalen Pfad (Entwicklung):

# Default HTTP (port 8010)
uvx --from /path/to/your/project/mcp mcp serve --transport http

# With custom port
uvx --from /path/to/your/project/mcp mcp serve --transport http --port 8080

# With custom host
uvx --from /path/to/your/project/mcp mcp serve --transport http --host 0.0.0.0 --port 8080

Aus dem Remote-Repository (empfohlen):

uvx --from \
  git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
  mcp serve --transport http --port 8080

Ausführen von einem Remote-Git-Repository

Sie können den MCP-Server auch direkt von einer Git-Repository-URL aus ausführen, ohne ihn lokal zu klonen, indem Sie uvx --from verwenden:

# Example from a specific branch and subdirectory
uvx --from \
  git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp

# Explanation:
# - git+https://... .git : The repository URL
# - @main   : The branch, tag, or commit hash
# - #subdirectory=mcp     : The folder within the repo containing the pyproject.toml

Dieser Befehl ruft die angegebene Version ab, installiert ihre Abhängigkeiten in einer temporären Umgebung und führt den Standardbefehl aus ( serve --transport stdio ).

Sie können nach dem URL-Fragment weitere Argumente wie --log-level DEBUG oder --mock hinzufügen:

uvx --from \
  git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
  --log-level DEBUG --mock

So führen Sie die Ausführung im HTTP-Modus von einer Remotequelle aus aus:

uvx --from \
  git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
  serve --transport http --port 8080

Weitere Optionen

Protokollierung

# Set logging level (applies to default stdio or explicit serve)
uvx --from /path/to/your/project/mcp mcp --log-level DEBUG
uvx --from /path/to/your/project/mcp mcp serve --transport http --log-level DEBUG

# Log to file (applies to default stdio or explicit serve)
uvx --from /path/to/your/project/mcp mcp --log-file jentic_mcp.log
uvx --from /path/to/your/project/mcp mcp serve --transport http --log-file jentic_mcp.log

Mock-Modus

Aktivieren Sie den Mock-Modus für die Entwicklung, ohne eine Verbindung zum eigentlichen Jentic API Knowledge Hub herzustellen:

# Mock mode with default stdio
uvx --from /path/to/your/project/mcp mcp --mock

# Mock mode with explicit HTTP
uvx --from /path/to/your/project/mcp mcp serve --transport http --mock

Umgebungsvariablen

Stellen Sie Umgebungsvariablen mithilfe einer .env Datei bereit:

# Env file with default stdio
uvx --from /path/to/your/project/mcp mcp --env-file .env

# Env file with explicit HTTP
uvx --from /path/to/your/project/mcp mcp serve --transport http --env-file .env

Verwendung mit Claude

Das MCP-Plugin kann mit Claude oder anderen LLMs verwendet werden, die die MCP-Spezifikation unterstützen:

Vom Remote-Repository ausführen (empfohlen):

# Run the server in HTTP mode first
uvx --from \
  git+https://github.com/jentic/jentic-tools.git@main#subdirectory=mcp \
  mcp serve --transport http --port 8000

# Then connect claude-cli
claude-cli --mcp http://localhost:8000

Vom lokalen Pfad ausführen (Entwicklung):

# Run the server in HTTP mode first
uvx --from /path/to/your/project/mcp mcp serve --transport http --port 8000

# Then connect claude-cli
claude-cli --mcp http://localhost:8000

Entwicklung

Ausführliche Entwicklungsanweisungen finden Sie unter CLAUDE.md .

Paketstruktur

  • src/mcp/ : Haupt-MCP-Paket

    • transport/ : Transportimplementierungen (HTTP, stdio)

    • mock/ : Mock-Datenanbieter für die Entwicklung

    • tools.py : Werkzeugdefinitionen

    • handlers.py : Anforderungshandler

    • main.py : CLI-Einstiegspunkte

    • adapters/ : Adapterimplementierungen

    • core/ : Kernfunktionalität

  • tests/ : Testsuite

Testen

# Ensure dev dependencies are installed: pdm install -G dev
pdm run test

Lintierung und Formatierung

Verwendet ruff , black , isort , mypy über pdm-Skripte.

# Run all linters/formatters
pdm run lint

# Run only ruff
pdm run linter

Lizenz

Proprietär - Jentic

Available Tools

3 tools
executeC

Perform the chosen action for the user using the provided details (if any are needed).

ParametersJSON Schema
NameRequiredDescriptionDefault
execution_typeYesSpecify whether to execute an 'operation' or a 'workflow'.
inputsYesThe input parameters required by the operation or workflow.
uuidYesThe UUID of the operation or workflow to execute.

TDQS

C2.4/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden for behavioral disclosure. It states 'perform the chosen action' which implies a write/mutation operation, but doesn't disclose critical behavioral traits: whether this is synchronous/asynchronous, what permissions are required, whether it's idempotent, what happens on failure, or what the response contains. For a tool that appears to execute potentially complex operations/workflows, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that gets straight to the point without unnecessary words. However, this conciseness comes at the cost of being under-specified for a tool with three required parameters and no annotations - it could benefit from one more clarifying sentence about what gets executed.

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

Completeness2/5

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

For a tool with 3 required parameters (including a nested object), no annotations, and no output schema, the description is inadequate. It doesn't explain what types of actions can be executed, what the expected outcomes are, or provide any error handling context. The agent would struggle to use this tool effectively without trial and error.

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 the schema already documents all three parameters thoroughly. The description adds no parameter-specific information beyond the generic 'using the provided details' - it doesn't clarify the relationship between execution_type, uuid, and inputs, or provide examples of valid input objects. Baseline 3 is appropriate when the schema does all the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose2/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Perform the chosen action for the user using the provided details' is tautological - it essentially restates the tool name 'execute' without specifying what kind of actions or resources are involved. While it mentions 'chosen action' and 'provided details', it doesn't clarify whether this executes operations, workflows, scripts, or other entities, nor does it distinguish this from sibling tools like 'load_execution_info' or 'search_apis'.

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 minimal guidance - it mentions 'chosen action' and 'provided details' but gives no explicit when-to-use criteria, no prerequisites, and no comparison to alternatives like 'load_execution_info' (which presumably loads execution information rather than performing execution) or 'search_apis'. The agent must infer usage from the parameter schema alone.

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

load_execution_infoC

Get more details about a specific action (like what information it needs from the user) before confirming you want to run it. Usually used if the action requires specific inputs. Coding Agents: To provide tools to an LLM that you have selected at runtime, use this tool to retrieve the necessary information and save it to 'jentic.json' at the root of your project.

ParametersJSON Schema
NameRequiredDescriptionDefault
operation_uuidsYesThe UUIDs of the operations to include in the configuration files.
workflow_uuidsYesThe UUIDs of the workflows to include in the configuration files.

TDQS

C2.5/5.0
Behavior2/5

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

With no annotations provided, the description carries full burden but offers limited behavioral insight. It mentions retrieving information and saving to a file, but doesn't disclose critical traits like whether this is a read-only operation, if it modifies state, authentication needs, rate limits, or error handling. The description adds some context but leaves significant gaps.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is moderately concise but not optimally structured. The first sentence clearly states the purpose, but the second sentence adds implementation details for coding agents that may not apply to all users. The information is somewhat front-loaded but could be more focused.

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

Completeness2/5

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

Given no annotations and no output schema, the description should provide more complete context. It mentions retrieving details and saving to a file, but doesn't explain what format the details take, what 'jentic.json' contains, or what happens if parameters are invalid. For a tool with 2 required parameters and mutation implications, this is inadequate.

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 parameters are well-documented in the schema. The description doesn't add meaningful semantic context beyond what the schema provides about operation_uuids and workflow_uuids. It mentions 'configuration files' but doesn't clarify how parameters relate to this output.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool 'Get more details about a specific action' which indicates its purpose, but it's vague about what 'details' means and doesn't clearly differentiate from sibling tools like 'execute' or 'search_apis'. It mentions saving to 'jentic.json' which adds specificity but confuses the core purpose with implementation details for coding agents.

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 some guidance ('Usually used if the action requires specific inputs') but lacks explicit when-to-use vs. alternatives. It doesn't clarify when to choose this over 'execute' or 'search_apis', and the coding agent note is situational rather than general usage guidance.

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

search_apisA

Search for available actions or information based on what the user wants to do (e.g., 'find Discord servers', 'send a message'). Use this first to understand what's possible.

ParametersJSON Schema
NameRequiredDescriptionDefault
capability_descriptionYesNatural language description of the action needed (e.g., 'send emails', 'weather forecasting', 'natural language processing')
keywordsNoOptional list of specific keywords to help narrow down the search
max_resultsNoMaximum number of actions to return

TDQS

A3.6/5.0
Behavior2/5

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the tool searches for actions or information but lacks details on behavioral traits such as response format, error handling, rate limits, or authentication needs. For a search tool with zero annotation coverage, this is a significant gap, though it does imply it returns results (e.g., 'to return' in the schema).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded, with two sentences that efficiently convey purpose and usage guidelines. Every sentence earns its place by providing essential information without redundancy. It could be slightly more concise by integrating examples more seamlessly, but overall it's well-structured.

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 moderate complexity (3 parameters, no output schema, no annotations), the description is adequate but has clear gaps. It covers purpose and usage well but lacks behavioral details like return format or error handling. Without annotations or an output schema, the description should do more to compensate, making it minimally viable but incomplete.

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 the schema already documents all three parameters thoroughly. The description does not add any additional meaning beyond what the schema provides, such as explaining how 'capability_description' interacts with 'keywords' or detailing search algorithms. Baseline 3 is appropriate when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Search for available actions or information based on what the user wants to do.' It provides a specific verb ('search') and resource ('available actions or information'), with examples like 'find Discord servers' and 'send a message' to illustrate usage. However, it doesn't explicitly distinguish this from sibling tools like 'execute' or 'load_execution_info', which prevents a perfect score.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit guidance on when to use this tool: 'Use this first to understand what's possible.' This clearly indicates it should be employed as an initial step to discover capabilities before invoking other tools, effectively differentiating it from siblings like 'execute' (which likely performs actions) and 'load_execution_info' (which might retrieve details).

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.

  1. 3 tool updatesv1.0.0
    • First observedexecute
    • First observedload_execution_info
    • First observedsearch_apis

TDQS

C2.9/5.0

Scored across 3 tools

Disambiguation4/5

The tools have distinct primary purposes: 'search_apis' discovers available actions, 'load_execution_info' retrieves details for a specific action, and 'execute' performs the action. There is minor potential overlap between 'load_execution_info' and 'execute' in handling action details, but the descriptions clarify their sequential workflow roles.

Naming Consistency3/5

The naming is mixed: 'search_apis' and 'load_execution_info' use snake_case with descriptive verbs, while 'execute' is a single verb without a noun. This inconsistency in structure (two tools follow a verb_noun pattern, one does not) reduces predictability, though the names remain readable.

Tool Count4/5

With 3 tools, the count is reasonable for a server focused on action discovery and execution. It feels slightly thin but covers core workflows (search, detail retrieval, execution), avoiding bloat. A few more tools might enhance completeness, but this is a solid minimal set.

Completeness3/5

The tool set covers a basic workflow (search, load info, execute) for action-based operations, but there are notable gaps. For example, there's no tool for managing or listing previously executed actions, error handling, or status checks, which could limit agent effectiveness in more complex scenarios.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Related MCP Connectors

Related MCP Servers