neuron_get_approvalFetch a single approval request by id, including its decision once resolved. Set waitSeconds (1–60) to long-poll until it resolves. Requires a bot API key.
| Name | Required | Description | Default |
|---|---|---|---|
| apiKey | Yes | Bot API key with 'nrn_' prefix for authentication | |
| approvalId | Yes | The approval request id (UUID) | |
| waitSeconds | No | Long-poll up to N seconds for resolution |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint, providing safety expectations. The description supplements this by disclosing the long-polling behavior, inclusion of the decision once resolved, and the authentication requirement (bot API key). These are useful behavioral traits beyond the annotations, and there is no contradiction.
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 two sentences, front-loaded with the main purpose, and every sentence adds value: the primary action, the long-poll option, and the auth requirement. No redundant or vague phrasing.
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 the simplicity of the tool (2 required params), the rich annotations, and full schema coverage, the description is well-rounded. It explains what is fetched, the optional long-polling, and auth. No output schema exists, but the return value is implied ('including its decision'). Slightly more detail on the returned object could be added, but it's sufficient.
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 has 100% coverage with clear descriptions for all three parameters (apiKey, approvalId, waitSeconds). The description adds minimal semantics beyond the schema, merely restating waitSeconds' purpose and range. Baseline 3 is appropriate since the schema carries the load.
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 uses a specific verb ('Fetch') and clearly identifies the resource ('single approval request by id'), plus the additional context of including the decision once resolved. This distinguishes it from sibling tools like list_approvals or respond_approval.
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 for when to use it: fetching a single approval by id, and the optional long-polling behavior via waitSeconds. It doesn't explicitly mention alternatives, but the scope is evident from the wording; no exclusions are stated but are not necessary.
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.