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 as read-only, idempotent, and non-destructive. The description adds value by disclosing the scope (caller's subscriptions only), default filter (active), and the specific return fields. This goes beyond the annotations without contradicting them.
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 core action and resource, followed by return fields and usage guidance. Every sentence earns its place with no redundancy or filler.
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 one optional parameter, no output schema, and comprehensive annotations, the description is complete. It covers purpose, usage, return values, and default behavior. The sibling context (subscribe/unsubscribe) makes the use case clear.
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 (100% coverage). The description's mention of 'active subscriptions' implies the default behavior, but it does not add significant meaning beyond the schema. Baseline 3 is appropriate.
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 ('List') and resource ('the caller's active subscriptions'), clearly distinguishing this from sibling tools like subscribe and unsubscribe. It also specifies the returned fields, 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 explicitly states when to use it: 'before adding more' subscriptions (i.e., before subscribe) and 'to find an id to cancel' (before unsubscribe). It also implies the default behavior of showing only active subscriptions, with an optional include_inactive parameter for cancelled ones.
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 have heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, validate_claim, and discover_tools all route to the same 5,743-tool catalog, and the six Polymarket tools (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) all target prediction-market analysis. Long descriptions clarify some boundaries, but an agent could easily misroute a query to the wrong variant.
Names are mostly snake_case and readable, with consistent families like ask_pipeworx_* and polymarket_*, but conventions vary widely: verb-first (validate_claim, compare_entities, generate_llms_txt), noun-first (entity_profile, bet_research, recent_changes), and standalone verbs (remember, recall, forget). The pipeworx_ prefix is applied inconsistently, and get_memes sits apart from the data-tool naming style.
32 tools exceeds the comfortable range and the count is inflated by near-duplicate variants (three ask_pipeworx versions, six Polymarket tools). The scope mismatch compounds the issue: the server is named imgflip but nearly all tools belong to Pipeworx data/prediction-market functionality, so the set feels bloated rather than deliberately scoped.
As an imgflip/meme server the surface is severely incomplete: get_memes explicitly refers to caption_image for creating memes, but that tool is absent, creating a dead end. The remaining tools cover a broad but unrelated Pipeworx data domain, so no coherent domain gets full lifecycle coverage and the tool set fails its apparent core purpose.