neuron_fund_walletInitialize a wallet funding transaction via Paystack. Returns a checkout URL to complete the payment in a browser.
| Name | Required | Description | Default |
|---|---|---|---|
| Yes | Email address to receive the payment receipt | ||
| amount | Yes | Amount to fund in the smallest currency unit — kobo for NGN (e.g., 500000 = NGN 5,000) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations indicate a non-read-only, non-destructive mutation. The description adds that the tool returns a checkout URL for browser payment, but it does not disclose what happens if the payment is cancelled, whether the transaction ID is needed later, or if there are rate limits. More behavioral context would help.
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 sentences, no redundant words. The most important information (what it does and what it returns) is front-loaded.
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?
No output schema exists, so the description's mention of a 'checkout URL' provides essential context. However, it lacks details on the response format, authentication requirements, and error scenarios. For a financial transaction tool, this feels incomplete.
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 100% of parameters with descriptions. The amount description clarifies the unit (kobo) and provides an example. The email description specifies 'payment receipt', adding value beyond the schema type.
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 it initializes a wallet funding transaction via Paystack and returns a checkout URL. It distinguishes from sibling tools like neuron_fund_ad_request and neuron_wallet_balance by specifying the 'wallet' resource and the payment flow.
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 implies the tool is for wallet funding but does not explicitly state when to use it versus alternatives (e.g., neuron_fund_campaign). No prerequisites or exclusions are mentioned, leaving the agent to infer context.
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.