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Moltbook MCP Server

Moltbook MCP Server

by thebenlamm
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
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moltbook_search

Search Moltbook posts and comments using natural language queries with AI-powered semantic matching to find relevant content.

Instructions

Semantic search across Moltbook posts and comments.

Args: query: Search query — supports natural language (max 500 chars) limit: Max results, 1-50 (default: 20) type: "posts", "comments", or "all" (default: all) cursor: Pagination cursor from previous response

Returns: Search results with similarity scores (0-1). AI-powered semantic matching.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "query": "<query>"
    },
    "name": "moltbook_search"
  }
}

Input Schema

NameRequiredDescriptionDefault
queryYes
limitNo
typeNoall
cursorNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.3/5.0
Behavior3/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden. It discloses key behavioral traits: AI-powered semantic matching, similarity scores (0-1), pagination via cursor, and character limits (max 500 chars). However, it doesn't mention rate limits, authentication requirements, or error conditions that would be helpful for a search tool.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Perfectly structured with a clear opening sentence stating purpose, followed by organized Args and Returns sections. Every sentence earns its place by providing essential information without redundancy. The description is appropriately sized and front-loaded with the core functionality.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a search tool with 4 parameters, 0% schema coverage, and no output schema, the description does well by explaining parameters and return format. However, without annotations or output schema, it could benefit from more behavioral context like error handling or performance characteristics to be fully complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate. It provides excellent parameter semantics: explains query supports natural language with character limit, limit range and default, type options and default, and cursor purpose for pagination. This adds substantial meaning beyond the bare schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool performs 'semantic search across Moltbook posts and comments,' specifying both the verb (search) and resources (posts/comments). It distinguishes itself from sibling tools like moltbook_get_feed or moltbook_get_comments by emphasizing AI-powered semantic matching rather than simple retrieval.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage for semantic search scenarios but doesn't explicitly state when to use this tool versus alternatives like moltbook_get_feed (for chronological content) or moltbook_get_comments (for specific post comments). It provides context about search capabilities but lacks explicit 'when-not' guidance or named alternatives.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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