Reply to a post/comment as a Presence identity. Requires an attached session with valid auth cookies. CALL FORMAT: presence_reply({ identity_id: 'uuid', platform: 'x', target_id: 'tweet_id_here', content: 'Great point!' })
| Name | Required | Description | Default |
|---|---|---|---|
| content | Yes | Reply text. | |
| platform | Yes | Platform where the post lives. | |
| target_id | Yes | Post/tweet/comment ID to reply to. | |
| identity_id | Yes | Identity to reply as. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the auth requirement but does not detail behavioral traits such as rate limits, idempotency, error handling, or return behavior. Some transparency is present via the call format example.
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 and a code example, with no extraneous information. The purpose is front-loaded, making it easy for an agent to quickly understand the tool.
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 tool's mutation nature (replying), the description adequately covers the action and auth requirement. However, it omits details about the return value (e.g., success/failure indication) and any potential side effects. Minor gap.
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 descriptions for all four parameters. The description adds value by providing a call format example with concrete values (e.g., 'uuid', 'x', 'tweet_id_here'), which clarifies expected formats beyond the schema definitions.
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 tool's function: replying to a post/comment as a Presence identity. It uses a specific verb ('Reply') and resource ('post/comment'), distinguishing it from sibling tools like presence_post (creating new posts) and presence_act (a broader action).
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 mentions a prerequisite ('Requires an attached session with valid auth cookies') but does not explicitly state when not to use the tool or compare it to alternatives. Usage context is implied but not fully specified.
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.
Many tools have distinct purposes, but there are several overlapping or redundant tools (e.g., leadsignal vs leadsignal_generate, multiple code audit tools, multiple trading proposal/journal tools, and several 'universal' entry points like zambo_help, zambo_ask, zambo_universal). Descriptions help, but the volume creates ambiguity.
Naming conventions vary across prefixes (zambo_, zambot_, axis_, presence_, trading_, etc.), with some tools using single words (weather, translate) and others using verb_noun patterns. Aliases like leadsignal_generate for leadsignal break consistency. While prefixes provide some grouping, the overall pattern is mixed.
125 tools is excessive for a single MCP server, even if the server aims to be a universal stack. This makes it overwhelming for agents to navigate and increases the likelihood of misselection. Many tools could be split into domain-specific servers.
The tool surface is extraordinarily comprehensive, covering agent identity, cross-layer orchestration, code analysis, content generation, legal scanning, lead generation, trading, on-chain data, and more. Nearly any common agent task is supported with multiple tools, leaving few obvious gaps.