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, idempotentHint=true, destructiveHint=false, covering safety. Description adds return field details but does not contradict 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 sentences, directly stating purpose and usage. No unnecessary words.
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?
With one optional parameter, no output schema, and rich annotations, the description is sufficient. It lists return field names, providing helpful context.
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 coverage is 100%, and the schema describes the only parameter (include_inactive) with a clear description. The description adds no additional parameter info, so baseline 3.
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 lists the caller's active subscriptions and lists the return fields. It distinguishes from siblings 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?
The description provides explicit usage guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' It does not explicitly state when not to use it, but the context is 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.
Multiple tools have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, and ask_pipeworx_grounded is a subtle behavioral variant, creating a real selection hazard. The six polymarket_* tools also blur together (edges vs arbitrage vs fill_risk vs kalshi_spread all relate to finding and acting on mispricings), and scan_competitor_ai_presence is largely a wrapper over ai_visibility_check.
All names are snake_case and several families share clear prefixes (ask_pipeworx, polymarket_*, pipeworx_*, scan_*), which keeps the set readable. However, the set mixes verb-first names (get_sample, compare_entities, resolve_entity) with noun-first names (entity_profile, bet_research, recent_changes, polymarket_edges), and the _beta suffix signals a status while _grounded signals a behavior, so the pattern is not predictable.
33 tools is above the threshold where a tool set starts to feel bloated, and for a server named 'Biosamples' it is an extreme scope mismatch: 31 of 33 tools relate to Pipeworx data routing, prediction markets, memory, or subscriptions rather than biological samples. The count is also padded with near-duplicates such as ask_pipeworx_beta and scan_competitor_ai_presence.
Against the server's stated identity, the BioSamples surface is severely thin: only search_samples and get_sample exist, with no batch retrieval, project/group navigation, sample-group hierarchy, or submission/update path. The 31 unrelated tools do not fill this gap — they serve a completely different domain, so an agent using this server for biological sample data will hit dead ends.