Return all AI drafts waiting for approval (messages_approved=false). Each draft includes the contact name, headline, company and generated text. Used as input for bulk review by an AI assistant which can iterate through drafts and approve/reject them. Optionally filter by campaign_id.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Max rows (default 25, max 100) | |
| campaign_id | No | Optional: filter to a single campaign |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds context about the returned fields and purpose beyond the annotations (readOnlyHint, destructiveHint, idempotentHint are already present). No contradictions; it explains the function without repeating annotation details.
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?
The description is concise with two efficient sentences, front-loading the core purpose and then adding context and optional filter.
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?
For a read-only list tool with 2 parameters and good annotations, the description sufficiently covers usage context and filtering. No output schema is present, but the description mentions the included fields, which is adequate.
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 coverage is 100% with descriptions for both limit and campaign_id. The tool description rephrases these but doesn't add new semantic detail, meeting the baseline expectation.
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 clearly states the tool returns AI drafts waiting for approval with a specific condition (messages_approved=false) and lists the fields included. This distinguishes it from siblings like approve_message or reject_message.
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 explains the tool is for bulk review by an AI assistant to iterate and approve/reject drafts, and mentions optional campaign_id filtering. It provides clear context but does not explicitly state when not to use it.
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.
