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 mark it read-only, non-destructive, idempotent, and open-world. The description adds value by explicitly listing the return fields (id, type, params, created_at, last_fired_at, fire_count) and clarifying the 'caller's' scope. While it doesn't describe pagination or cancellation details, it exceeds the annotation baseline by detailing the response shape.
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, front-loaded with the operation, then return fields, then usage guidance. Every sentence serves a purpose with 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?
The description covers what the tool returns, who the data belongs to, and when to use it. There is no output schema, so listing the return fields is especially valuable. With one optional parameter and clear sibling context, this is complete for the tool's complexity.
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%: the single parameter include_inactive is fully described in the input schema. The description doesn't add parameter-specific meaning, but with full schema coverage the baseline of 3 applies. The phrase 'active subscriptions' hints at the default behavior but doesn't explicitly connect to the parameter.
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 itself from sibling tools like subscribe and unsubscribe by being a read-only listing operation. The mention of returned fields further clarifies what the tool does.
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?
Provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This tells the agent when to use it relative to subscribe/unsubscribe and gives clear use cases, forming a strong usage guideline.
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 tool clusters have genuinely fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grouned, and deep_research all route to the same 5,767 tools and differ only by use-case nuance, while polymarket_edges, polymarket_arbitrage, and bet_research all surface trading opportunities. scan_competitor_ai_presence is a thin wrapper over ai_visibility_check, and entity_profile, recent_changes, and compare_entities pull overlapping company data. The descriptions are detailed, but an agent can easily select the wrong tool in these clusters.
All tools use snake_case and family prefixes are consistent (polymarket_*, pipeworx, datalastic_*, scan_*, ask_*), making the set predictable and readable. The main deviation is verb placement — verb-first (list_subscriptions, resolve_entity, search_within) vs noun-first (entiy_profile, recent_alerts, bet_research) — and prefix position varies between ask_pipeworx and pipeworx_feedback, but these are minor.
33 tools exceeds the heavy threshold, and the count is padded by redundancy: four ask_pipeworx variants that are near-identical, six polymarket tools with overlapping scans, and wrapper tools like scan_competitor_ai_presence that just call ai_visibility_check. The server name suggests maritime focus but only two tools serve that domain, while the rest span a sprawling data-research, prediction-market, AI-visibility, and npm-scanning surface. Consolidating the ask_pipeworx family into one router with a mode parameter and merging wrappers would trim the set to roughly 20 tools without losing capability.
The core data-research lifecycle is thoroughly covered: resolve_entity feeds entity_profile, compare_entities, recent_changes, validate_claim, and deep_research, and the prediction-market workflow includes discovery, edge detection, fill-risk validation, and cross-venue analysis. Subscriptions, memory, and feedback are well supported. Minor gaps exist — the datalastic maritime piece has only live position lookups (no history or fleet tools), and one-offs like generate_llms_txt and scan_dependency feel unrelated — but there are no critical dead ends.