X feedback discovery, noise-reduced query planning, signal filtering, and AI handoffs.
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Average 4.2/5 across 3 of 3 tools scored.
Each tool has a clearly distinct purpose: searching/planning, filtering signals, and building a handoff package. There is no overlap or ambiguity.
All tools follow the consistent 'hyperxosist_verb_noun' pattern with snake_case: build_handoff, filter_signals, search_plan.
Three tools is well-scoped for the focused domain of X feedback processing. Each tool earns its place without being excessive or insufficient.
The tool surface covers planning, filtering, and packaging, but lacks data collection (fetching/scraping X posts). Agents must obtain X data externally, creating a notable gap in the workflow.
hyperxosist_build_handoffAUse to turn previously collected X feedback into a structured Signal-to-Fix package and coding-agent prompt. It does not perform general summarization, search the web, scrape X, or modify source code.
| Name | Required | Description | Default |
|---|---|---|---|
| feedback | Yes | Previously collected X feedback text. | |
| productName | Yes |
| Name | Required | Description |
|---|---|---|
| type | Yes | |
| handoff | Yes | |
| agentPrompt | Yes | |
| signalToFixInput | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and destructiveHint. The description adds context that the tool does not perform general summarization, search, or scraping, which clarifies its behavioral boundaries. There is no contradiction with annotations since the tool likely generates output without side effects.
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 description is two sentences long, front-loaded with the primary purpose and then exclusions. Every sentence serves a purpose; there is no fluff.
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?
Given the tool's simplicity (two parameters, nested output via output schema), the description covers the transformation and constraints. The output schema likely documents return values, so the description need not elaborate. It is reasonable for an agent to invoke correctly.
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?
The schema covers 50% of parameters with descriptions (only 'feedback' is described). The tool description does not add any new information about parameters beyond what the schema provides. The 'productName' parameter lacks both schema and description explanation, leaving ambiguity.
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 clearly states the tool's purpose: converting X feedback into a structured Signal-to-Fix package and coding-agent prompt. It explicitly lists what it does not do (summarization, web search, etc.), distinguishing it from sibling tools like hyperxosist_filter_signals and hyperxosist_search_plan.
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 description starts with 'Use to...' indicating when to use the tool. It also states what it does not do, preventing misuse. However, it does not explicitly compare to siblings or provide exclusions beyond the listed negatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hyperxosist_filter_signalsAUse after X posts or tweet text have already been collected. Separates actionable bugs, feature requests, and UX friction from empty praise, engagement bait, and spam. It does not fetch, scrape, or search X.
| Name | Required | Description | Default |
|---|---|---|---|
| feedback | Yes | Previously collected X post text to classify in memory. |
| Name | Required | Description |
|---|---|---|
| keep | Yes | |
| type | Yes | |
| discard | Yes | |
| summary | Yes | |
| keepCount | Yes | |
| discardCount | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint, idempotentHint, and destructiveHint false. The description adds that the tool does not fetch, scrape, or search, reinforcing its non-destructive nature. No additional behavioral traits (e.g., rate limits) are mentioned, but the existing annotations and description provide adequate transparency.
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 description is only two sentences long, yet it conveys all essential information: when to use, what it does, and what it avoids. No unnecessary words.
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?
Given the tool's simplicity (single parameter, no nested objects, output schema exists), the description is complete. It covers usage context, functionality, and boundaries. No further elaboration is needed.
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 coverage is 100%, with a single parameter 'feedback' described in the schema as 'Previously collected X post text to classify in memory.' The description does not add new parameter-level details but aligns well with the schema. Baseline 3 is appropriate.
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 clearly states the tool's purpose: separating actionable signals (bugs, feature requests, UX friction) from non-actionable ones (empty praise, etc.). It also explicitly states what it does not do (fetch, scrape, search), distinguishing it from potential sibling tools.
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 description provides explicit guidance: 'Use after X posts or tweet text have already been collected.' It also clarifies what the tool does not do, which helps the agent decide when not to use it. However, it does not directly mention alternative tools for fetching or searching.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
hyperxosist_search_planAUse only for specialized X (Twitter) research planning: complaints, bug reports, feature requests, product feedback, or community signals. Builds multiple noise-reduced official x.com/search URLs and quality scores. It is not general web search and does not scrape X or collect posts.
| Name | Required | Description | Default |
|---|---|---|---|
| intent | Yes | An X-specific research goal, for example: Find user complaints and bug reports on X about Acme. |
| Name | Required | Description |
|---|---|---|
| type | Yes | |
| mission | Yes | |
| queries | Yes | |
| missionId | Yes | |
| searchUrls | Yes | |
| paymentPolicy | Yes | |
| qualityScores | Yes | |
| estimatedCostUsd | Yes | |
| requiresPaymentForAutomatedProductionUse | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnly, idempotent, non-destructive. Description adds that it builds noise-reduced URLs and quality scores, and does not scrape or collect posts. This is useful but modest; no mention of response format or authorization, which output schema partially covers.
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?
Two sentences, each earning its place. First sentence states purpose and usage scope; second sentence clarifies exclusions. No redundancy or unnecessary detail.
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?
With only one parameter, high schema coverage, and an output schema, the description is fully adequate. It covers purpose, usage boundaries, and behavioral constraints. No missing information for a tool of this complexity.
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?
The single parameter 'intent' is fully described in the schema with an example. Description enriches by listing specific intent categories (complaints, bug reports, etc.), adding meaning beyond the schema's generic 'research goal'.
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?
Clearly states it's for specialized X (Twitter) research planning, lists specific use cases (complaints, bug reports, etc.), and distinguishes from general web search and scraping. The verb 'search_plan' combined with 'builds URLs and quality scores' defines a specific resource and action.
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?
Explicitly limits usage to X research planning and provides examples of when to use. Also clarifies what it is not (not general web search, not scraping). However, it does not explicitly compare to sibling tools (build_handoff, filter_signals), which could help further.
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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