neuron_add_tool_secretStore or update a secret credential (API key, token, etc.) for a custom tool integration. Secrets are encrypted at rest.
| Name | Required | Description | Default |
|---|---|---|---|
| id | Yes | Unique identifier (UUID) of the tool to add the secret to | |
| key | Yes | Secret key name used in templates (e.g., 'API_KEY', 'AUTH_TOKEN') | |
| value | Yes | Secret value to store securely (will be encrypted) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds value beyond annotations by noting that secrets are 'encrypted at rest,' which is a behavioral detail not captured in annotations. It aligns with the idempotentHint and destructiveHint annotations (non-destructive, safe to repeat).
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 extremely concise: two sentences that cover purpose, security, and context. It is front-loaded with the key action and resource, wasting no 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?
For a simple credential management tool with full schema coverage, the description is complete. It communicates the essential purpose and security feature. However, it could optionally mention that it works with existing tool IDs, but this is already implied by the 'id' parameter.
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?
The input schema covers all three parameters with descriptions, achieving 100% coverage. The description does not add parameter-specific meaning beyond the schema, but the schema itself is adequate. Hence a baseline score of 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 description clearly states the verb ('Store or update'), the object ('secret credential'), and the context ('for a custom tool integration'). It distinguishes the tool from its sibling 'neuron_remove_tool_secret' by focusing on storage/update rather than removal.
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 provides context ('for a custom tool integration') but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. The distinction from 'neuron_remove_tool_secret' is implicit rather than explicit.
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.