list_subscriptionsList the caller's active subscriptions. Returns id, type, params, created_at, last_fired_at, fire_count for each. Use this to review what you're monitoring before adding more or to find an id to cancel.
| Name | Required | Description | Default |
|---|---|---|---|
| include_inactive | No | Include cancelled subscriptions in the response (default false). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, and idempotentHint=true. The description adds the specific return fields but does not elaborate on behavioral traits beyond what annotations provide. It does not contradict 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 sentences covering action, return fields, and usage guidance. It is concise, front-loaded, and contains no filler.
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?
The tool is simple with one optional parameter and no output schema. The description explicitly lists the return fields and provides usage context, making it complete 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 input schema has one optional parameter (include_inactive) with full schema description. The tool's main description does not add extra meaning to this parameter beyond the schema, so baseline 3 applies.
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 lists the caller's active subscriptions and lists the fields returned. It distinguishes from sibling tools like subscribe and unsubscribe, which are action-oriented.
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 context for when to use it: to review monitored subscriptions before adding more or to find an ID to cancel. It does not explicitly state when not to use it or list alternatives, but the guidance is clear.
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.