x402-twitter-search
Twitter Search: Search recent tweets. X API v2. Bearer token.
Input Schema
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Q to process | |
| count | No | Count to process | |
| query | No | Query to process |
Twitter Search: Search recent tweets. X API v2. Bearer token.
| Name | Required | Description | Default |
|---|---|---|---|
| q | No | Q to process | |
| count | No | Count to process | |
| query | No | Query to process |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the burden of behavioral disclosure. It usefully discloses the auth mechanism and API version, but it does not mention rate limits, pagination, read-only behavior, or failure modes.
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 definition is compact and front-loaded: purpose appears first, followed only by API version and auth requirement. There is no filler or redundant wording.
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?
For an external API tool with no output schema and no annotations, this is too thin. It omits which parameters are required, the difference between q and query, result count semantics, return-value shape, and error behavior.
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?
Schema description coverage is 100%, which establishes a baseline of 3. The description implies that q/query are search terms and count controls result quantity, but it does not resolve the confusing q-versus-query duplication or clarify what 'Count to process' actually means.
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 gives a specific verb and resource: 'Search recent tweets.' It also names the underlying API version (X API v2), which helps distinguish this from generic search tools like x402-search or x402-web-search.
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 phrase 'Search recent tweets' implies the tool is for finding recent Twitter/X posts, which provides some usage context. However, it never names alternatives, states when not to use it, or explains prerequisites beyond bearer-token authentication.
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.
The tool set is saturated with near-duplicates and synonyms: character-count vs char-count, clamp vs clamp-value, is-abundant vs is-abundant-num vs is-abundant-number, and fetch vs browser-scrape vs web-scrape vs text-scrape. Generic names like 'difference', 'normalize', 'range', and 'partition' make the boundaries even harder for an agent to determine.
Most tools share a x402- kebab-case prefix, but the set mixes noun-only names (math, hash, prime, time), verb-first names (get_stats, find, validate), auto-generated names (x402-publish-1787853294312-base-account), and inconsistent variants like temp vs temperature vs temperature-convert. This is not a coherent verb_noun convention despite the common prefix.
1677 tools is an extreme count that creates selection paralysis and makes coherent agent use impractical. A utility or marketplace server at this scale needs sub-services or namespacing rather than a flat tool list.
The surface has broad token coverage across many utility categories, but the marketplace aspect is incomplete: service_discovery and get_stats exist, yet there are no generic publish, update, delete, or account-management operations. Utility families also contain redundant variants without clear completion or lifecycle structure.