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 indicate read-only, idempotent, non-destructive. Description adds return fields and default behavior (active only), 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?
Three sentences, each essential: purpose, fields returned, usage guidance. No fluff, front-loaded with key info.
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?
Complete for a listing tool with good annotations. Covers purpose, parameters, return structure, and usage context. No gaps.
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 has 100% coverage for the single parameter. Description does not add further meaning, but consistency is maintained. Baseline score 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?
Identifies specific verb (list) and resource (subscriptions), distinguishes from siblings (subscribe/unsubscribe) and includes return fields.
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 states when to use: 'review what you're monitoring before adding more or to find an id to cancel', providing clear context and alternatives.
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.
Multiple tools have unclear boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical, ai_visibility_check overlaps with scan_competitor_ai_presence, and the six Polymarket tools all orbit the same edge-detection concept. Descriptions are detailed, but an agent would frequently have to read long text to decide which near-overlapping tool to call.
Naming is mostly snake_case but semantically inconsistent: some names are verb-led (ask_pipeworx, validate_claim, remember), some noun-led (polymarket_edges, entity_profile), and some use a vendor prefix (scrapingdog_scrape, scrapingdog_amazon_product). The polymarket_edges vs polymarket_edge_tracker singular/plural pairing adds further confusion.
34 tools is above the 25+ threshold and the count is not justified by a single clear purpose. The server is named Scrapingdog but most tools are unrelated Pipeworx research, memory, subscription, and prediction-market functionality, making the set feel overstuffed and unfocused.
The data-research and subscription/memory lifecycles are fairly complete, with create/read/delete coverage for those areas. However, relative to the Scrapingdog scraping identity, the surface is thin: only three scraping tools exist, and there is no direct way to fetch a Pipeworx record by URI or manage scraped-data artifacts.