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Marketo MCP Server

by alexleventer

marketo_add_lead_to_list

Add one or more leads to a Marketo static list by providing the list ID and an array of lead IDs, up to 300 per request.

Instructions

Add one or more leads to a static list by list ID. Accepts an array of lead IDs. Max 300 leads per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
listIdYes
leadIdsYes

Implementation Reference

  • src/index.ts:388-400 (registration)
    Registration of the 'marketo_add_lead_to_list' tool via server.tool() with name, description, schema, and handler.
    server.tool(
      'marketo_add_lead_to_list',
      'Add one or more leads to a static list by list ID. Accepts an array of lead IDs. Max 300 leads per call.',
      {
        listId: z.number(),
        leadIds: z.array(z.number()),
      },
      tool(async ({ listId, leadIds }) =>
        makeApiRequest(`/rest/v1/lists/${listId}/leads.json`, 'POST', {
          input: leadIds.map(id => ({ id })),
        })
      )
    );
  • Input schema for the tool: listId (z.number()) and leadIds (z.array(z.number())).
    {
      listId: z.number(),
      leadIds: z.array(z.number()),
    },
  • Handler function that calls makeApiRequest with POST to /rest/v1/lists/{listId}/leads.json, mapping leadIds to the required Marketo input format.
    tool(async ({ listId, leadIds }) =>
      makeApiRequest(`/rest/v1/lists/${listId}/leads.json`, 'POST', {
        input: leadIds.map(id => ({ id })),
      })
    )
  • The makeApiRequest helper function used by the handler to execute the Marketo REST API call.
    async function makeApiRequest(
      endpoint: string,
      method: string,
      data?: any,
      contentType: string = 'application/json'
    ) {
      const token = await tokenManager.getToken();
      const headers: Record<string, string> = {
        Authorization: `Bearer ${token}`,
      };
    
      if (contentType) {
        headers['Content-Type'] = contentType;
      }
    
      try {
        const response = await axios({
          url: `${MARKETO_BASE_URL}${endpoint}`,
          method,
          data:
            contentType === 'application/x-www-form-urlencoded'
              ? new URLSearchParams(data).toString()
              : data,
          headers,
        });
        return response.data;
      } catch (error: any) {
        console.error('API request failed:', error.response?.data || error.message);
        throw error;
      }
    }
  • The tool() wrapper helper that handles response formatting and error wrapping for tool handlers.
    function tool<T>(handler: (args: T) => Promise<unknown>) {
      return async (args: T) => {
        try {
          const response = await handler(args);
          return {
            content: [{ type: 'text' as const, text: JSON.stringify(response, null, 2) }],
          };
        } catch (error: any) {
          return {
            content: [
              {
                type: 'text' as const,
                text: `Error: ${error.response?.data?.message || error.message}`,
              },
            ],
            isError: true,
          };
        }
      };
    }
Behavior4/5

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

With no annotations, the description carries the burden. It discloses important behavioral traits: accepts array of IDs and a maximum of 300 leads per call. However, it does not mention idempotency, error handling, or prerequisites.

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 with no extraneous words. The first sentence states the core purpose, the second adds key context (array format and limit). All content is essential.

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 no output schema and no annotations, the description covers the main aspects: purpose, parameters, and a critical limit. It lacks return value info and error conditions, but for a simple add operation it is reasonably complete.

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 description coverage is 0%, so the description adds meaning by explaining that listId identifies a static list and leadIds is an array of lead IDs. This matches the required parameters and clarifies their purpose.

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 (add), the resource (leads to a static list), the input method (by list ID), and key details (accepts array, max 300). This differentiates it from siblings like marketo_remove_lead_from_list.

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

Usage Guidelines3/5

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

The description does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention scenarios to avoid (e.g., dynamic lists, batches over 300). It only describes the operation.

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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