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 read-only and non-destructive behavior. Description adds caller scope and return field details, fully disclosing behavior.
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 wasted words, front-loaded with the core 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?
For a simple list tool with no output schema, it covers purpose, return fields, and usage. Could optionally mention ordering or pagination but not necessary.
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%, so the description does not need to add parameter details. It does not introduce new information beyond the schema for 'include_inactive'.
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. Distinguishes from siblings like subscribe/unsubscribe by specifying review purpose.
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 monitored items before adding more or to find an ID to cancel. Lacks explicit 'when not to use' but 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.
Many tools have distinct purposes with thorough descriptions, but there is meaningful overlap among ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research, all of which route questions to data sources. The multiple polymarket tools (edges, arbitrage, fill_risk, edge_tracker, kalshi_spread) also require careful reading to differentiate. The urlscan tools (domain, ip, search, submit, result) are distinct, but the overall set mixes several unrelated domains, increasing misselection risk.
Naming is a mix of conventions: short urlscan verbs (domain, ip, search, submit), noun-first names (entity_profile, recent_changes, deep_research), verb_noun names (compare_entities, resolve_entity, generate_llms_txt), and prefixed families (polymarket_*, pipeworx_*). There is no uniform verb_noun pattern or consistent prefix convention across the set. This inconsistency makes it hard to predict what a tool does from its name alone.
With 36 tools, the set is far above the 3-15 range typical for a coherent server, even for a broad data API. The inclusion of meta-tools like discover_tools and suggest_questions suggests the count is so high that agents need help navigating it. The load is compounded by tools spanning urlscan.io, Pipeworx, prediction markets, memory, subscriptions, and feedback, making the server feel like a grab bag rather than a focused service.
The urlscan portion is complete for searching, submitting, and retrieving scan results, and the Pipeworx side covers a wide range of data and analysis capabilities. However, the server is named 'Urlscan Io' while most tools are unrelated to urlscan, creating a mismatch between the stated purpose and the actual surface. There are no obvious gaps for the included features, but the lack of a coherent domain makes it hard to assess what 'complete' means for this set.