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 readOnlyHint=true, destructiveHint=false, and idempotentHint=true, so the safety profile is covered. The description adds value by specifying the return fields (id, type, params, created_at, last_fired_at, fire_count) and scoping to the caller's subscriptions. This is useful context beyond the annotations, though it does not elaborate on rate limits or pagination.
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: the first states the core purpose and return fields, the second provides usage guidance. Every word earns its place, and the information is front-loaded for quick comprehension.
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 low-complexity tool with one optional parameter and comprehensive annotations, the description is complete. It covers the purpose, return shape, and appropriate usage scenarios, leaving no significant gaps for an agent to select and invoke the tool correctly.
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%, with the include_inactive parameter already described as 'Include cancelled subscriptions in the response (default false).' The description's mention of 'active subscriptions' aligns with the parameter semantics but does not add additional meaning. Thus, the baseline 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 clearly states the tool's function: listing the caller's active subscriptions. It specifies the resource (subscriptions) and the verb (list), and the inclusion of 'caller's' and 'active' distinguishes it from sibling tools like subscribe and unsubscribe.
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 the tool: to review current monitoring before adding more (i.e., before subscribe) or to find an id to cancel (i.e., before unsubscribe). This provides clear context and implies the alternatives, making the usage guidance actionable.
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.
Most tools have clearly distinct roles, but several overlapping pairs create ambiguity: ask_pipeworx vs ask_pipeworx_beta are explicitly identical today, discover_tools vs suggest_questions both serve discovery/onboarding, and bet_research vs polymarket_edges both address betting-edge questions. The detailed descriptions help, but an agent could still select the wrong tool in these cases.
The set uses at least four naming conventions: get_* for Bluesky reads, verb_noun for Pipeworx tools (ask_pipeworx, resolve_entity, validate_claim), polymarket_* prefixed tools, and verb-only memory tools (remember, recall, forget). Each subgroup is internally consistent, but the overall mix feels inconsistent and unpredictable.
39 tools is well beyond the typical well-scoped server, and the scope sprawls across Bluesky reads, Pipeworx data, Polymarket analysis, memory, subscriptions, and one-off utilities like generate_llms_txt and scan_dependency. The count would be more reasonable split into separate servers; as-is it feels heavy and unfocused.
Within the server's evident scope, coverage is strong: Bluesky read operations, Pipeworx query/research/verification, entity profiling, and subscription lifecycle are all represented. The main gaps are write actions for Bluesky (posting, following, liking) and a few auxiliary features that are only partially integrated, but no critical workflow dead-ends appear for the primary data-research use cases.