Skip to main content
Glama
BACH-AI-Tools

Twitter Api45 MCP Server

about_profile

Retrieve Twitter profile registration details and usage information by entering a username to access account data and activity insights.

Instructions

Returns the information about the profile's registration and usage

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
screennameYesExample value: elonmusk
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. While 'Returns' implies a read-only operation, the description omits critical details: what specific data constitutes 'registration and usage', whether the lookup is real-time or cached, rate limits, or error conditions (e.g., private accounts).

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

Conciseness4/5

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

The description is a single, efficient sentence with no redundant phrases. However, extreme brevity becomes a liability given the lack of annotations and output schema—conciseness here manifests as under-specification rather than disciplined clarity.

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?

Despite having only one simple parameter, the tool lacks an output schema. The description fails to compensate by explaining what data structure is returned or what 'registration and usage' information actually includes (e.g., creation date, tweet frequency, login history), leaving significant gaps in the agent's understanding.

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 100% description coverage with the 'screenname' parameter fully documented including an example value ('elonmusk'). The description adds no parameter-specific context, but given the schema completeness, it meets the baseline expectation without penalty.

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

Purpose3/5

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

The description uses a clear verb ('Returns') and identifies the resource ('profile's registration and usage'), but 'registration and usage' remains vague (creation date? activity metrics?). Crucially, it fails to differentiate from siblings like 'user_info' or 'profiles_by_restids', leaving the agent uncertain which profile tool to select.

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

Usage Guidelines1/5

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

The description provides zero guidance on when to use this tool versus the 20+ sibling user/profile lookup tools available. There is no mention of prerequisites, specific use cases, or conditions where this tool is preferred over 'user_info' or 'profiles_by_restids'.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/BACH-AI-Tools/bachai-twitter-api45'

If you have feedback or need assistance with the MCP directory API, please join our Discord server