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 provide readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is covered. The description adds value by specifying the returned fields (id, type, params, created_at, last_fired_at, fire_count) and clarifying that by default only active subscriptions are listed. No contradiction with 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?
The description is extremely concise: two sentences that front-load the purpose and return fields, then give concrete usage guidance. Every sentence earns its place without redundancy or unnecessary elaboration.
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 list tool with one optional parameter, the description is complete: it states the purpose, return format, default behavior (active only), and specific use cases. The sibling tools (subscribe/unsubscribe) make the context clear, and the annotations cover safety semantics, so the description fills the remaining gap effectively.
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 description coverage is 100% for the only parameter include_inactive, with a clear description already in the schema. The tool description adds no additional semantic detail about this parameter beyond the schema, so 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 ('List') with a clear resource ('the caller's active subscriptions') and enumerates the return fields. It distinguishes itself from sibling tools like subscribe and unsubscribe by explicitly focusing on reviewing active subscriptions and finding IDs to cancel.
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 tells the user when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This implies when not to use it (e.g., for subscribing or unsubscribing) and names the alternative actions, making the usage context clear.
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 tight clusters of overlapping tools: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all answer factual questions (beta is currently identical to stable per its own description), six Polymarket tools all surface betting/edge opportunities, and ai_visibility_check vs scan_competitor_ai_presence duplicate functionality. Despite long descriptions, an agent could easily pick the wrong tool.
Naming mixes bare single-word nouns (address, block, node, stats, transaction), bare verbs (remember, recall, forget, subscribe), verb_noun compounds (generate_llms_txt, scan_dependency, compare_entities), and prefixed families (polymarket_*, pipeworx_*, ask_pipeworx*). Some clusters are internally consistent, but the blockchain endpoints break the verb convention entirely and there is no uniform pattern across the set.
36 tools spanning at least six unrelated domains — blockchain explorer, structured-data research, prediction markets, AI visibility, memory, and subscriptions — is too heavy for a coherent server. The count exceeds the 25+ threshold and reflects scope creep rather than a focused purpose.
The Pipeworx research surface is near-complete (discover/resolve/ask/ground/verify/search-within plus entity/profile/compare), prediction markets are exhaustively covered, and memory/subscriptions have full lifecycles. But the server's namesake domain — Blockchair blockchain data — is thin at just five basic queries with no fee estimation, mempool, or deeper chain analytics, leaving notable gaps in the surface implied by the server name.