robots_txtFetch a sample robots.txt from httpbin.org (/robots.txt). Use to test robots.txt parsing or as a content-type placeholder.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| body | Yes | Sample robots.txt content | |
| status | Yes | HTTP status code | |
| content_type | Yes | Content-Type header |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, openWorldHint, idempotentHint, and destructiveHint, so the safety profile is fully declared. The description adds that it fetches from a specific URL, which is useful but minimal extra context beyond annotations.
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 short sentences, no wasted words, and immediately conveys the tool's action and purpose.
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 a parameterless sample fetch tool with an output schema, the description provides sufficient information: the exact URL and two use cases. No further context 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?
There are no parameters, and the schema has 100% coverage (empty). Baseline is 4 for zero-parameter tools. The description does not need to add parameter details.
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 explicitly states it fetches a sample robots.txt from httpbin.org, and provides two specific use cases: testing robots.txt parsing or as a placeholder. This clearly distinguishes it from sibling tools like 'get' or 'post'.
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 gives clear context on when to use this tool (testing parsing or placeholder), but does not mention when not to use or alternatives. However, the purpose is so specific that it is not ambiguous.
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.
Many tools serve overlapping purposes: ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, compare_entities, and resolve_entity all perform data lookups with subtle differences. The prediction-market tools (bet_research, polymarket_arbitrage, polymarket_edges, etc.) heavily overlap, and even the HTTP utilities (headers, ip, user_agent, cookies) echo similar request information. Agents will struggle to pick the right tool.
Naming is internally inconsistent: some tools use short imperative verbs (get, post, status, delay), others use long descriptive phrases (ask_pipeworx, entity_profile, scan_competitor_ai_presence). There is no common pattern—some are verb+noun, some noun+noun, some proper nouns. The mix of styles makes it hard to predict tool names.
47 tools is excessive for a server named Httpbin, which conventionally should have a handful of HTTP debugging utilities. Most tools are unrelated to HTTP (data lookups, prediction markets, memory, subscriptions), indicating severe scope creep. The count feels bloated and unwieldy.
For HTTP debugging, the set is incomplete—missing common methods (PUT, DELETE, PATCH) and error-handling features. For the broader data/proposition-market domain, coverage is fragmented and unclear. The server appears to be a jumble of partially complete feature sets with no coherent domain, leaving obvious gaps in each.