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vanman2024

Multilead Open API MCP Server

by vanman2024

get_leads_from_campaign

Retrieve and filter leads from a specific campaign using advanced criteria including status, email verification, company, occupation, and timeline parameters to target relevant prospects.

Instructions

Retrieve leads from a specific campaign with advanced filtering

Supports 7 groups of filters with OR logic within groups and AND logic between groups:

  1. Advanced filters (name, company, occupation, headline)

  2. Status filters (status, connection degree, out of office)

  3. Email verification filters

  4. Current step filter

  5. Selected leads filter

  6. Step change timestamp filter

  7. General search filter

Args: user_id: The ID of the user account_id: The ID of the account (seat) campaign_id: The ID of the campaign search: Search leads by fullName, email, company, headline, etc. filter_by_verified_emails: Filter leads with verified emails filter_by_not_verified_emails: Filter leads without verified emails filter_by_status: Filter by status ([1]=Discovered, [2]=Connection pending, [3]=Connected not replied, [4]=Replied) filter_by_connection_degree: Used with filter_by_status=[4] for additional status filtering ([1]=replied connected, [2,3]=replied not connected) filter_by_current_step: Filter leads on specific campaign steps filter_by_name: Filter leads whose names contain this value filter_by_company: Filter leads whose company contains this value filter_by_occupation: Filter leads whose occupation contains this value filter_by_headline: Filter leads whose headline contains this value filter_by_out_of_office: Filter leads with "Out of office" status filter_by_step_change_timestamp: Filter leads with stepChangeTimestamp greater than this filter_by_selected_leads: Retrieve specific leads by their IDs limit: Number of results to return (default: 30) offset: Pagination offset (default: 0)

Returns: List of leads matching the filter criteria with pagination metadata

Example: get_leads_from_campaign( user_id="123", account_id="456", campaign_id="789", filter_by_status=[4], filter_by_connection_degree=[1], filter_by_verified_emails=True, limit=50 )

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
user_idYes
account_idYes
campaign_idYes
searchNo
filter_by_verified_emailsNo
filter_by_not_verified_emailsNo
filter_by_statusNo
filter_by_connection_degreeNo
filter_by_current_stepNo
filter_by_nameNo
filter_by_companyNo
filter_by_occupationNo
filter_by_headlineNo
filter_by_out_of_officeNo
filter_by_step_change_timestampNo
filter_by_selected_leadsNo
limitNo
offsetNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the filtering logic (7 groups with OR/AND logic), pagination behavior (limit/offset defaults), and return format (list with pagination metadata). It doesn't mention rate limits, authentication requirements, or error conditions, but provides substantial operational context.

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 well-structured with clear sections (overview, filter groups, args, returns, example) but could be more concise. Some parameter explanations are verbose, and the filter group listing could be streamlined. However, information is front-loaded with the core purpose first.

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?

Given the tool's complexity (18 parameters, advanced filtering logic), no annotations, and the presence of an output schema, the description provides comprehensive context. It explains filtering semantics, parameter usage, return format, and includes a practical example, making it complete enough for effective tool invocation.

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

Parameters5/5

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

With 0% schema description coverage for 18 parameters, the description fully compensates by providing detailed explanations for each parameter, including enum mappings for status and connection degree filters, default values for limit/offset, and clarification of parameter relationships (e.g., filter_by_connection_degree used with filter_by_status=[4]).

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's purpose with a specific verb ('Retrieve') and resource ('leads from a specific campaign'), and distinguishes it from siblings by specifying 'advanced filtering' capabilities. The opening sentence immediately communicates the core function.

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 implies usage through the detailed filter explanations but doesn't explicitly state when to use this tool versus alternatives like 'get_leads_from_seat' or 'list_leads'. It provides context about filtering capabilities but lacks explicit guidance on tool selection criteria.

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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