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). |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, etc. Description adds specific return fields and mentions filtering by active/inactive subscriptions, enhancing transparency 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 clearly convey purpose and usage with no extraneous information. Front-loaded with the main action.
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 the tool's simplicity (no required params, one boolean), the description covers purpose, usage, and output fields adequately without needing an output schema.
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% and the parameter 'include_inactive' is fully described in the schema. The description does not add extra meaning beyond what the schema provides.
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 'List the caller's active subscriptions' and enumerates return fields. 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 advises using this tool 'to review what you're monitoring before adding more or to find an id to cancel', providing clear context for appropriate use.
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.
Tool purposes are mostly distinct, with detailed descriptions differentiating similar-sounding tools like ask_pipeworx, ask_pipeworx_grounded, and deep_research. However, some overlap exists between bet_research and polymarket_* tools, requiring careful reading of descriptions to select the correct one.
Tool names mix verb_noun patterns (e.g., resolve_entity, validate_claim) with noun_phrases (e.g., entity_profile, recent_alerts) and occasional inconsistencies like pipeworx_trending or search_within. While most names are readable, the lack of a uniform convention makes it harder to predict tool names.
32 tools cover a broad domain of data retrieval, analysis, prediction markets, and system management. The count is high but reasonable given the extensive feature set, though a more focused set could improve coherence.
The tool set covers major needs: data lookup, comparison, validation, monitoring, and memory. Missing CRUD operations for external data are expected as this is a read-centric API, so gaps are minor and don't hinder common tasks.