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 declare readOnly, idempotent, and non-destructive hints, so the safety profile is covered. The description adds useful behavioral context: it returns the caller's subscriptions (auth scoping), includes only active ones by default, and specifies the exact fields returned. This goes beyond what annotations provide, though it doesn't mention pagination or rate limits.
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, front-loaded with the purpose, and every sentence earns its place: one states functionality and return fields, the other gives usage context. 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?
For a simple read-only list tool with one optional parameter and no output schema, the description is complete. It explains the return fields, default behavior (active only), and practical use cases, making it entirely self-sufficient for an agent to select and 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?
Schema coverage is 100% with a single parameter, include_inactive, which already has a clear description. The tool description aligns with that by saying 'active subscriptions' but adds no extra meaning beyond the schema. Baseline 3 is appropriate because the schema does the heavy lifting.
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 starts with a specific verb and resource: 'List the caller's active subscriptions.' It clearly distinguishes this from sibling tools like subscribe and unsubscribe by focusing on listing existing subscriptions, and even enumerates the returned fields (id, type, params, etc.), making the purpose unambiguous.
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 explicit usage context: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to invoke the tool in relation to subscribe/unsubscribe actions, though it doesn't explicitly name alternatives like list_feeds. The guidance is clear and actionable.
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.
Several tools occupy nearly the same niche: ask_pipeworx_beta is explicitly an identical duplicate of ask_pipeworx when no experiment is active, and ask_pipeworx, ask_pipeworx_grounded, deep_research, and validate_claim all overlap as question-answering entry points. Other families like entity_profile vs compare_entities vs recent_changes and ai_visibility_check vs scan_competitor_ai_presence also blur together despite long disambiguating descriptions.
All tool names use a clean, readable snake_case style, and there are strong prefix families like ask_pipeworx, polymarket_, list_, and scan_. However, the set is not uniformly verb_noun: entity_profile, deep_research, recent_alerts, pipeworx_trending, and several others are noun phrases rather than actions, so the pattern is mostly consistent but not strict.
34 tools is well beyond the 25+ threshold where a server starts feeling bloated, and the server name 'Space Feeds' suggests a narrow niche while most of the surface is a general data research, prediction-market, memory, and subscription platform. Each tool may be useful, but as a set the scope is sprawling rather than focused.
The major workflows have good lifecycle coverage: data lookup and grounded verification, entity resolution and profiling, prediction-market analysis, memory (remember/recall/forget), subscriptions (subscribe/list/unsubscribe/recent_alerts), and feed reading (list/read/fetch) are all represented. Minor gaps include a direct pipeworx:// citation reader and feed curation or management operations, but agents can work around those.