AI analysis of a contact list — breakdown by industry, title, company size, location, and engagement level.
| Name | Required | Description | Default |
|---|---|---|---|
| query | No | Which contacts to analyze — e.g. 'my leads', 'contacts added this month' | |
| campaign_id | No | Analyze contacts in a specific campaign |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, so the agent knows it's safe. The description adds the context that it's 'AI analysis', implying computational processing, and lists the output dimensions. However, it does not disclose any potential latency, credit consumption, or that it might produce a report versus a structured list. Given the annotations cover the safety profile, this is acceptable but not rich.
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 a single, focused sentence that front-loads the tool's purpose and lists the analysis dimensions. No wasted words.
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?
The description is moderately complete: it explains the input scope (contact list) and expected output aspects (breakdown by key dimensions). However, it lacks details on the output format (e.g., report, JSON, etc.) and whether the analysis is synchronous or async. Given the absence of an output schema, additional clarity would help, but the core use case is clear.
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 both query and campaign_id having descriptive text. The tool description adds no parameter-specific detail beyond the schema, but the breakdown dimensions in the description help interpret what 'query' might filter. Baseline is 3.
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 identifies the tool's function: performing AI analysis on a contact list, with specific breakdown dimensions (industry, title, company size, location, engagement level). This distinguishes it from sibling tools like search_contacts or export_contacts, which are retrieval/export operations. The verb 'analysis' is implicit but the noun phrase is specific.
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 does not explicitly state when to use this tool versus alternatives like search_contacts or run_ai_report. Usage is implied by the name and description, but there is no guidance on prerequisites or 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.
Several tools occupy nearly identical roles (create_contact/add_contacts/save_contacts_to_crm/upload_contacts; add_companies/create_company; search_contacts/search_crm_contacts/find_contacts_at_companies), and the names don't clearly reveal whether they operate on saved or prospected data. Although the descriptions clarify some boundaries, an agent would frequently need to read many descriptions carefully to avoid mis-selection.
Most tools follow a snake_case verb_noun pattern such as list_campaigns, create_deal, and update_contact. Minor deviations like company_intelligence, get_icp, load_more_contacts, and the inconsistent use of add_/create_/upload_/save_ for similar creation actions keep it from being fully consistent.
51 tools is far beyond the well-scoped range and exceeds the 50+ extreme threshold. Even for a broad CRM/prospecting/marketing platform, exposing this many tools at once makes agent selection costly and unwieldy.
Contact and company creation/read/update are covered, but delete is absent across the board, and campaigns can be drafted, listed, and measured but not edited, activated, paused, or deleted. Deals, forms, and landing pages also lack update/delete lifecycle actions, creating meaningful dead ends for common CRM workflows.
