contextium-mcp-server

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list_workflow_connectors

List third-party connectors (GitHub, Grafana, etc.) assigned to a workflow. Retrieve connector IDs and base URLs to use with connector_request.

Instructions

List the third-party connectors allocated to a workflow (GitHub, Grafana, etc.). Use this to discover what connector_id values you can call with connector_request. You never see any access token — only connector ids and base URLs.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "workflowId": "<workflowId>"
    },
    "name": "list_workflow_connectors"
  }
}

Input Schema

Name	Required	Description	Default
`workflowId`	Yes	The workflow whose connectors to list.
`workspaceId`	No	Workspace ID. Defaults to your default workspace.

Schema Changelog

Changes observed during successful MCP inspections.

First observedJul 10, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses a key behavioral trait: 'You never see any access token — only connector ids and base URLs.' This informs the agent that authentication credentials are not exposed, which is critical for understanding the tool's capabilities and limitations.

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 extremely concise: two sentences. The first sentence states the purpose, and the second provides the usage rationale. Every word earns its place, with no redundancy or filler.

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?

Given the tool's simplicity, no output schema, and only two parameters, the description covers the essential aspects: what is listed (connectors), what is returned (ids and base URLs), and what is not (access tokens). It is complete enough for an agent to use without confusion.

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%, so the baseline is 3. The description does not add significant meaning beyond the schema, except implicitly tying workflowId to the workflow context. The workspaceId is explained in the schema, and the description does not need to elaborate further. A higher score would require additional parameter-specific guidance.

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 lists third-party connectors for a workflow, with specific examples (GitHub, Grafana). It distinguishes from the sibling connector_request by noting that this tool is used to discover connector_id values. The verb 'list' is specific and the resource 'workflow connectors' is well-defined.

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 instructs to use this tool to discover connector_id values for use with connector_request. While it does not list when not to use it, the context is clear given the sibling set. It provides a direct pointer to the next step, which is helpful for an AI agent.

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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