neuron_list_flow_runsList execution runs for a flow, newest first. Each run shows status (running/waiting/completed/failed/cancelled), trigger type, step count, and timing.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | UUID of the flow. | |
| page | No | Page number (default: 1). | |
| limit | No | Items per page (default: 20, max: 100). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark it read-only, idempotent, and non-destructive; the description adds the sort order ('newest first') and the projected output fields (status, trigger type, step count, timing). It does not mention rate limits or authentication, but those are not material for this read-only list tool.
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 efficient sentences; the main purpose and ordering are front-loaded, followed by compact output-field detail. No 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 a simple read-only list operation, 100% schema parameter coverage, and no output schema, the description provides the key return fields and ordering, making it complete for correct invocation. It does not describe the response envelope, but the enumerated fields cover the operative content.
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 covers all three parameters with descriptions (UUID, page default 1, limit default 20 max 100), so the description does not add parameter-level detail. It only contextualizes the resource ('for a flow'), which is already reflected in the schema. Baseline 3 is appropriate.
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 opening clause names a specific operation ('List execution runs for a flow') and the resource ('flow'), with an explicit ordering ('newest first'). The second sentence spells out returned fields, so an agent can distinguish it from single-run tools like neuron_get_flow_run.
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 gives clear context: use this to enumerate runs belonging to a flow, and the pagination params support browsing. It does not explicitly name alternative tools or state when not to use it, e.g., for single-run detail use neuron_get_flow_run, so it lacks explicit exclusion guidance.
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 are clearly separated by resource type, but there is meaningful overlap in messaging entry points (send_message, send_whatsapp, compose_message, bot_api_send) and contact ingestion/sync tools (import_contacts, populate_contacts, sync_whatsapp_contacts). The descriptions help disambiguate, but with 309 tools an agent will frequently need to read closely to pick the right one.
The overwhelming majority of tools follow a consistent verb_noun snake_case pattern: create_*, get_*, list_*, update_*, delete_*. Minor deviations like sales_stats, lead_stats, wallet_balance, and whoami break the pattern slightly, but overall naming is highly predictable.
309 tools is an extreme count for any MCP server, even a broad platform. This creates significant cognitive load and navigation overhead for agents, and far exceeds the well-scoped 3-15 tool range where coherence is strongest.
The tool surface is remarkably comprehensive across bots, contacts, campaigns, flows, knowledge bases, personas, marketplace, wallet, and products. Minor gaps exist — lead sources lack update/delete tools, and there is no single get_task or get_webhook alongside their list/update/delete counterparts — but these are workable gaps rather than dead ends.