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 and destructiveHint=false. The description adds behavior beyond annotations: it notes that only active subscriptions are returned by default and that inactive ones can be included with a parameter. It also discloses the exact return fields, which is valuable given no output schema.
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 are concise and well-structured: first states purpose, second lists return fields and usage guidance. No wasted 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 no output schema, the description fully covers return values with explicit field names. It also explains the default behavior and why you'd use the tool. For a low-complexity list tool with one optional parameter, this is 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 description coverage is 100% since include_inactive has a clear description ('Include cancelled subscriptions in the response (default false)'). The description only reinforces that 'active' is the default, adding no new parameter semantics beyond the schema.
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 uses a specific verb and resource: 'List the caller's active subscriptions.' It clearly distinguishes from siblings like subscribe/unsubscribe by focusing on listing existing subscriptions and specifying the exact fields returned.
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?
It provides clear usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implies when to use the tool and its role in the workflow, though it doesn't explicitly mention alternatives like subscribe/unsubscribe.
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.
Most tools have clearly distinct purposes, but there is some overlap among similarly named tools like ask_pipeworx, deep_research, and bet_research, which could cause misselection. However, detailed descriptions help differentiate them.
Tool names follow a mix of patterns (verb_noun, noun_noun, etc.) and use different prefixes (polymarket_, sec_8k_, pipeworx_), which is somewhat inconsistent but still readable and descriptive overall.
34 tools is on the higher side, with several tools dedicated to specific subdomains (e.g., 6 Polymarket-related, 4 SEC 8-K tools). While each has a distinct role, the number feels slightly bloated for a single server.
The tool surface covers a broad range of data research and monitoring tasks, including filings, entity profiles, claims, and prediction markets. Minor gaps exist (e.g., no data writing tools), but core workflows are well-supported.