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siscale

Arcanna Input MCP Server

by siscale

get_external_input_jobs

Retrieve Arcanna external input jobs configured with an API key to send data via HTTP calls. Use this to identify jobs ready for data ingestion.

Instructions

    Retrieve Arcanna External Input Jobs.
    An Arcanna External Input Job refers to a job where the user's API Key is configured on the input integration, enabling data to be sent via HTTP calls.
    Use this tool only when:
        - 1. The user intends to send data to Arcanna
             Example user query: "I want to start pushing/send data to Arcanna. What jobs can I use?"
        - 2. The user specifically requests jobs configured with an external API Key
             Example user query: "Can you show me the jobs that use an external API key input?"

    DO NOT use this tool outside the specific scenarios described. If you're unsure, ask the user for clarification before proceeding.
    DO NOT use this tool if the user is requesting a generic job retrieval, such as:
        - 1. "What are the available jobs?"
        - 2. "What jobs are in Arcanna?"
        - 3. "List the jobs."
        - 4. "Show the jobs."
        In such cases, use a different tool designed for general job listing instead.

Returns:
--------
list
    A list of dictionaries, each representing job details with the following keys:

    - job_id (int): Unique identifier for the job.
    - category (str): Category of the job.
    - title (str): Title or name of the job.
    - status (str): Current status of the job (e.g., ENABLED - the job is ingesting events. DISABLED - the job is stopped.
     READY_TO_SELECT_FEATURES - user must select decision points. etc.).
    - retrain_state (str): State of the retraining process.
    - retrain_msg (str): Message providing details about the retraining process.
    - labels (list of str): List of decision labels associated with the job.
    - features (list of str): List of decision points used in the job.
    - processed_documents_count (int): Number of events processed.
    - feedback_documents_count (int): Number of events that received feedback.
    - last_processed_timestamp (str): Timestamp of the last processed event.
    - last_feedback_timestamp (str): Timestamp of the last received feedback.
    - last_train_start_timestamp (str): Timestamp when the last training started.
    - last_train_finished_timestamp (str): Timestamp when the last training finished.
    - invalid (bool): Indicates whether the job is invalid (True/False).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

No annotations provided, so description carries full burden. It describes the return type and fields in detail, implying a read operation. However, it does not explicitly state the absence of side effects, auth requirements, or potential errors (e.g., empty result set).

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?

Well-structured with bullet points and front-loaded purpose. Slightly verbose in the return list but each element adds value. Could be slightly more concise, but effective.

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?

Comprehensive for a zero-parameter tool with no output schema. Provides detailed return field descriptions, clear usage guidelines, and sufficient context to understand when to invoke.

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?

No parameters, schema_description_coverage is 100%, so baseline is 4. The description adds context about what the tool returns, which is appropriate since the schema is empty.

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 'Retrieve Arcanna External Input Jobs' and explains the specific resource. It distinguishes from sibling tools by specifying when to use, using specific 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.

Usage Guidelines5/5

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

Provides explicit when-to-use scenarios with examples, and clearly lists cases where the tool should NOT be used, directing to a different tool for general job listing. This helps the agent avoid misuse.

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

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