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Funkel AI

create_list

Create a new lead list scoped to the request's app. Use this when the user wants to organise leads into a named bucket (e.g. 'Q4 Targets', 'Hot Founders'). The new list starts empty; populate it later via approve_leads with action='approve' and the new list_id, or by editing the list manually. Over MCP: call this tool once, without confirmation_token. It runs right away and returns the result.

Input Schema

NameRequiredDescriptionDefault
nameYesDisplay name for the list (1-100 chars)
descriptionNoOptional one-sentence description of what the list is for

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already declare non-read-only, non-destructive, non-idempotent, closed-world. The description adds real context beyond that: the list starts empty, how to populate it later, and the MCP calling convention (call once, no confirmation_token, runs immediately). The 'call this tool once' guidance helpfully reinforces the non-idempotent hint.

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?

Purpose and scoping are front-loaded, and the sentences largely earn their place by covering usage, follow-up path, and calling convention. The MCP-specific sentence adds a bit of length but carries actionable information.

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?

For a two-param create tool with no output schema, the description covers purpose, trigger, state semantics, follow-up, and invocation behavior. It only gestures at the return value ('returns the result'), which is the one remaining gap.

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

Parameters3/5

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

Schema description coverage is 100% with only 2 params, so the schema already documents both fields fully. The description adds name examples ('Q4 Targets', 'Hot Founders') but no syntax or constraint detail beyond the schema's 1-100 char range. Baseline 3 is appropriate.

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?

States a specific verb+resource (create a new lead list) and adds the scoping constraint (scoped to the request's app), which distinguishes it from siblings like get_list, list_lists, and rename_list. An agent can identify the operation without opening the schema.

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

Usage Guidelines4/5

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

Gives a clear when-to-use trigger ('when the user wants to organise leads into a named bucket') and names the follow-up path (approve_leads with action='approve'). It does not explicitly state when *not* to use it, nor does it mention the closer sibling import_search_leads_to_list, so it falls short of a 5.

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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