Twitter241 MCP Server

autocomplete

Generate Twitter search query suggestions from a partial input. Retrieve matching terms to streamline search entry and improve discovery.

Instructions

Twitter Seach Query Autocomplete

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": {
      "value": "<value>"
    },
    "name": "autocomplete"
  }
}

Input Schema

Name	Required	Description	Default
`value`	Yes	Example value: MrB

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.0Aug 9, 2026

TDQS

C2.6/5.0

Behavior1/5

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

With no annotations, the description is the sole source of behavioral information. It merely states the tool's purpose without disclosing behavior such as the format of suggestions, whether it performs network requests, or any side effects. The description adds no transparency beyond the name itself.

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

Conciseness3/5

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

The description is extremely brief (five words) and front-loaded, but it is incomplete as a sentence and contains a typo. While conciseness is a positive, it sacrifices clarity and completeness, making it minimally acceptable rather than well-structured.

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

Completeness2/5

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

For a simple tool with one parameter and no output schema, the description should at least explain what the tool returns (e.g., a list of suggestions) and how the input is used. The current fragment leaves the agent to guess the expected behavior, making it incomplete in context.

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?

The input schema has a single parameter 'value' with an example ('MrB'), providing some semantics. Schema description coverage is 100%, so the baseline is 3. The tool description does not add any additional meaning about how the parameter affects autocomplete behavior, but the example gives a hint.

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

Purpose4/5

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

The description 'Twitter Seach Query Autocomplete' clearly identifies the tool's purpose: providing autocomplete suggestions for Twitter search queries. It distinguishes itself from sibling tools like search_twitter by focusing on the autocomplete step rather than executing searches. However, it is a noun phrase rather than a full sentence with a verb, and contains a typo ('Seach').

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

Usage Guidelines2/5

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

No guidance is provided on when to use this tool versus alternatives. The description does not mention context, preconditions, or how it relates to other search tools. Without explicit usage instructions, the agent must infer the appropriate scenario.

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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