Get recent skill requests from the BytesAgain community wall, newest first. Returns id, title, request text, platform, budget, nickname, view_count, and created_at. Contact info is excluded for privacy. Optionally filter by keyword in title or request text.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | Number of requests to return. Default: 20. Max: 50. | |
| query | No | Optional keyword to filter requests by title or content. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full transparency burden. It explicitly states ordering ('newest first'), return fields, privacy handling ('Contact info is excluded for privacy'), and optional filtering. It does not mention authentication or side effects, but the read-only nature is clear and no destructive behavior is implied.
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?
Three sentences, front-loaded with purpose, and every sentence provides value: what it does, what it returns, and how to filter. No fluff or repetition.
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?
No output schema, so the description compensates by listing return fields explicitly. It lacks details on pagination or how to get older requests, and the response shape (array vs object) isn't stated. For a straightforward list tool, this is largely sufficient but not fully complete.
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%, so the schema already documents both parameters. The description adds that query filters by 'title or request text', which is present in the schema description too. No substantial new meaning is added, so 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 starts with a specific verb and resource: 'Get recent skill requests from the BytesAgain community wall, newest first.' This clearly differentiates from sibling tools like search_skills (skills) and submit_request (creating requests). The return fields and filter option further clarify scope.
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 clear context for when to use the tool: to retrieve recent skill requests, with optional keyword filtering. It doesn't explicitly exclude alternatives or name sibling tools, but the 'community wall' and 'requests' framing make it distinct from skill-focused tools.
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.
Several tools have overlapping purposes: evaluate_skill and scan_skill both assess skill safety, while generate_usecase, get_workflow, and score_skills all involve skill scoring and recommendation. Description differences exist but boundaries are not always crisp, potentially causing misselection. The unrelated get_deals tool also adds confusion.
Tool names mostly follow a consistent verb_noun snake_case pattern (e.g., search_skills, get_skill, submit_request). Minor inconsistencies exist: popular_skills uses an adjective instead of a verb, and generate_usecase uses 'usecase' while search_use_cases uses 'use_cases'.
With 14 tools, the server is on the higher end of the typical range but still well-scoped for its broad functionality (search, evaluation, workflows, community, content pipeline). Each tool serves a distinct functional area, though a few could be consolidated.
The core workflow of searching, retrieving, and evaluating skills is well covered, including use cases and community requests. However, there are minor gaps such as lack of a category browsing tool or direct single-skill installation, and the inclusion of unrelated AliExpress deals seems out of place.
