List contacts in a specific lead list with ID, name, headline, company, profile URL, gender and current status (e.g. invite_sent, connected, replied). The AI picks IDs from this list for further tools.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max results (default 50, max 200) | |
| offset | No | Offset for pagination (default 0) | |
| list_id | Yes | UUID of the lead list |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds context by listing returned fields and the role of providing IDs, without contradicting annotations.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences front-load the purpose and key details. Every sentence adds value without redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple list operation and good schema/annotations, the description adequately covers purpose, return fields, and usage. The mention of output fields compensates for the lack of output schema.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The description does not add meaning beyond the schema for parameters; it only mentions the list context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description specifies the verb 'List' and the resource 'contacts in a specific lead list', along with enumerated fields (ID, name, etc.). This clearly distinguishes it from sibling tools like get_contact_profile or enrich_contacts.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description states that 'The AI picks IDs from this list for further tools', indicating when to use it. While it does not explicitly list alternatives, the clear purpose helps differentiate from similar tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Most tools have distinct purposes, but there is some overlap among search tools (search_job_postings, search_google_xray, search_linkedin_people, search_web) and messaging tools (send_connection_request, send_linkedin_message, reply_to_chat). However, detailed descriptions clarify the differences.
All tools follow a consistent verb_noun snake_case pattern (e.g., add_contacts_to_campaign, list_campaigns). No mixing of conventions.
48 tools is high but justifiable given the broad domain (LinkedIn outreach, CRM, campaigns, job postings, etc.). However, some tools could be consolidated (e.g., multiple search tools).
The tool set covers the entire workflow: searching, connecting, messaging, campaign management, CRM operations (fields, stages, tasks, notes), job postings, and posting. No obvious gaps.
