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 the tool read-only, idempotent, and non-destructive. The description adds useful context about caller scoping, the 'active subscriptions' default filter, and the exact returned fields, which is valuable behavioral detail beyond the 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 concise sentences front-load the purpose and then provide essential details about return fields and use cases. Every sentence earns its place with no redundant wording.
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 one-optional-parameter read-only list tool, the description and schema together cover scope, output shape, and intended usage. The annotations cover safety, and no output schema is needed given the explicit field list.
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?
The only parameter, include_inactive, is fully documented in the input schema with a default value. The description does not mention it, but because schema coverage is 100%, the baseline score of 3 applies.
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 uses a specific verb-noun pair ('List the caller's active subscriptions') and clearly distinguishes this read-only tool from mutation siblings like subscribe and unsubscribe. It also specifies the resource scope and enumerates return fields, making the purpose unmistakable.
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 gives explicit use cases: 'review what you're monitoring before adding more' and 'find an id to cancel.' This implies when to use it versus subscribe/unsubscribe, though it does not name an alternative tool explicitly.
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 fuzzy boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all overlap on 'find and query data,' while the five polymarket_* tools plus bet_research form a heavily overlapping prediction-market cluster. The three genuine InterPro tools are clear, but an agent would frequently struggle to pick the right meta-tool.
Most tools follow a readable snake_case verb-first pattern like compare_entities, resolve_entity, and validate_claim. However, bare verbs (remember, forget, recall), product-prefixed nouns (pipeworx_feedback, pipeworx_trending), and variant suffixes (ask_pipeworx_beta, ask_pipeworx_grounded) break the pattern enough to feel inconsistent.
34 tools is well above the typical well-scoped range, and many tools duplicate or partially overlap each other's functionality. The count is further inflated by unrelated domains—AI visibility, prediction markets, memory, subscriptions, package auditing—bundled into a server nominally named 'Interpro.'
The InterPro subset (search_entries, get_entry, entries_for_protein) is minimal and lacks obvious protein/proteome-level operations, while the rest of the server covers so many unrelated domains that no single domain has clear end-to-end coverage. The Pipeworx query side is broad, but the overall surface feels like several incomplete toolsets merged rather than one complete product.