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doppelgangersai

Context API MCP Server

search_relevant_posts

Find specific posts from a Twitter/X user by describing what you're looking for in natural language.

Instructions

Semantic search of contextualized post renderings of a certain Twitter/X user, based on a natural language query. Twitter/X username and platform (= X) must be provided. Use this tool to find specific posts, relevant to the query.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query. Use natural language to describe what you're looking for. For instance: * "What does @elonmusk think about AI regulation?" or * "What is @hosseeb's prediction on the price of Bitcoin?"
usernameYesTwitter/X username to search within (without @). This argument is required.
platformNoPlatform to search. Currently only 'X' (Twitter) is supported.X
Behavior2/5

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

No annotations exist, and the description fails to disclose behaviors such as rate limits, authentication requirements, or how results are ordered. Only a high-level 'semantic search' is mentioned.

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?

Two concise sentences with no fluff. The first sentence defines the core function; the second provides usage context.

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

Completeness3/5

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

For a search tool with 3 parameters and no output schema, the description is minimal but adequate. It lacks details on result format or search behavior, which could be inferred.

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

Parameters3/5

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

Schema coverage is 100% with detailed query examples. The description merely restates requirements already in schema, adding no extra meaningful context about parameters.

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 on a user's posts using natural language. It distinguishes from sibling 'get_all_user_posts' by focusing on relevance to a query.

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

Usage Guidelines3/5

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

The description encourages use 'to find specific posts' but does not explicitly contrast with alternatives like 'get_all_user_posts' or provide when-not-to-use guidance.

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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