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 and destructiveHint=false. The description adds value by specifying the exact fields returned (id, type, params, created_at, last_fired_at, fire_count) and implies no side effects, which exceeds what annotations provide.
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 with no wasted words. The first sentence states the core purpose and output, and the second provides usage guidance, making it front-loaded and efficient.
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?
Given the tool's simplicity, complete annotations, and no output schema needed, the description fully informs the agent about what the tool does, what it returns, and when to use it. No gaps are present.
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?
There is only one optional parameter (include_inactive) with a clear description in the schema (100% coverage). The tool description does not repeat this, but the schema already handles it adequately, so 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 clearly states 'List the caller's active subscriptions' with a specific verb and resource. It also lists returned fields, distinguishing it from sibling tools like 'subscribe', which creates subscriptions, and 'unsubscribe', which cancels them.
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 says 'Use this to review what you're monitoring before adding more or to find an id to cancel,' providing clear context on when to use this tool versus 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.
Several tools occupy heavily overlapping territory: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) circle the same opportunity-finding domain, and entity_profile/recent_changes/compare_entities/validate_claim are all company-research flavored. While each pair has a stated distinction, an agent would frequently need to read deep into long descriptions to avoid misselection.
All names use snake_case, so there is no style chaos, but the pattern is inconsistent: some tools are verb-first (remember, validate_claim, resolve_entity), some are noun-first (entity_profile, recent_changes, alpaca_snapshot), and many use domain prefixes (ask_pipeworx*, polymarket_*, pipeworx_*) that overlap with bare verb-noun tools. The mix is readable but does not give a predictable clue to what a tool does.
34 tools is well above the 25+ threshold, and the count is inflated by redundant/overlapping surfaces: a beta duplicate, six overlapping Polymarket tools, and multiple meta/discovery tools (discover_tools, suggest_questions, pipeworx_trending). Only three tools actually relate to the server's stated Alpaca identity, making the scope feel sprawling rather than well-scoped.
The research/query surface is broad and covers a lot of workflows (single lookups, grounded verification, deep research, entity profiles, comparisons, change feeds). But there are notable gaps: pipeworx:// citation URIs are promised as resolvable yet no read/fetch tool exists, subscriptions cannot be updated (only created/cancelled), and the Alpaca tools are read-only with no account/order surface despite the server name. These are workaround-able but would cause friction or dead ends.