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 indicate read-only, idempotent, non-destructive behavior. Description adds value by detailing the specific return fields and the optional include_inactive parameter.
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, no filler. Every sentence adds necessary information.
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 simple list tool with one optional parameter and no output schema, description fully explains return structure and usage context.
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% (include_inactive is documented). Description adds no additional semantic meaning 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?
Clearly states it lists the caller's active subscriptions and enumerates return fields (id, type, etc.). Distinguishes from sibling tools like subscribe and unsubscribe.
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 tells when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Provides clear context.
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, especially within their domains (e.g., Polymarket tools are well-separated). However, a few tools like ask_pipeworx, deep_research, and suggest_questions could cause minor confusion, as they all deal with querying data.
Tools from the same service use consistent prefixes (linear_, polymarket_, pipeworx_), but the overall naming style is mixed: some are verb_noun (linear_create_issue), some are noun_verb (bet_research), and some are single words (remember). This inconsistency reduces predictability.
With 35 tools, the server covers a broad range of functionality (data query, prediction markets, memory, etc.). While not excessive, the count is on the higher side, and the server name 'Linear' suggests a narrower focus, which may mislead expectations.
The tool set covers core data querying, research, entity profiles, prediction market analysis, and memory operations comprehensively. Minor gaps exist (e.g., limited Linear CRUD), but the overall surface feels complete for its intended use as a data assistant.