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 provide readOnlyHint, openWorldHint, etc. Description adds return field names and default active filtering, which is helpful 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?
Two sentences, purpose first, no superfluous 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?
No output schema but description lists return fields. Covers purpose, usage, and parameters adequately. Minor gap: no mention of pagination or ordering, but tool is simple.
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 covers single parameter with description. Description does not explicitly explain the parameter but implies its effect via 'active subscriptions.' High schema coverage (100%) keeps baseline at 3.
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?
Description clearly states 'List the caller's active subscriptions' with specific verb and resource, distinguishes from siblings like subscribe/unsubscribe, and lists returned fields.
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 states when to use: 'to review what you're monitoring before adding more or to find an id to cancel.' Lacks explicit when-not-to-use or alternative 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.
While most tools have detailed descriptions, the large number of similar data-querying tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, bet_research, etc.) and overlapping domains (Polymarket, company research, medical) create ambiguity for an agent.
Tool names mix conventions: snake_case (ai_visibility_check, ask_pipeworx), camelCase absent, some with 'pipeworx' prefix, others not (bet_research, compare_entities). No consistent verb_noun pattern.
33 tools for a server named 'Medical Codes' is excessive and misaligned. The vast majority of tools cover unrelated domains (finance, prediction markets, general research), making the count inappropriate for the stated purpose.
For medical coding, only three tools exist (search_icd10, search_loinc, search_medical_terms). The rest are tangential or unrelated, leaving severe gaps in medical code coverage.