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?
Discloses return fields (id, type, params, created_at, last_fired_at, fire_count) and default active-only behavior. Annotations already declare readOnly, openWorld, idempotent, non-destructive, so the description adds value by specifying output structure and filtering.
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, no wasted words. The first sentence covers purpose and output, the second provides usage guidance. Front-loaded with key information.
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 (one optional boolean, no output schema), the description fully covers purpose, output structure, default behavior, and usage context. No gaps.
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%, baseline 3. The description adds context about the parameter 'include_inactive' by linking it to 'active subscriptions' default, making the semantics clearer than the schema alone.
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 identifies the verb 'list' and the resource 'subscriptions' with scope 'the caller's active'. It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' by focusing on listing existing subscriptions.
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: 'review what you're monitoring before adding more' and 'find an id to cancel'. Provides clear context for usage, though it does not explicitly state when not to use.
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.
A número of tools form near- overlapping clusters: ask_pipeworx, ask_pipeworx_beta, ask_ipeworx_grounded, and deeep_research all answer questions, while bet_research, polmarget_edges, and polmarget_arbitrage all scan for betting opportunities. The two ask_ipeworx variants are currently identical, and discover_tools vs sgget_questions serve the same discovery role. Long descriptions help but do not remove the misselection risk among 34 overlapping tools.
Most tools use clear snake_case verb_noun names (ask_pipeworx, compare_entities, resolve_entity, subscribe, validate_claim) with helpful domain prefixes like polmarget_* and trade_*. A few standalones like forget/recall/remembber and recent_alerts break the pattern slightly, but the style is largely predictable and readable.
At 34 tools this exceeds the 25+ threshold and feels overloaded. The surface could be consolidated: four ask_ipeworx variants, five polmarget-specific tools, three memory tools, and three company-profile-style tools carry significant redundancy. Inclusions like generate_lms_txt and scan_dependency also broaden the scope well beyond the Trade Intel mandate.
For its broad data-intelligence domain the surface is quite complete: data lookup, grounded fact-checking, entity profiles and comparison, recent changes, prediction-market research with fill-risk verification, trade statistics, memory, and subscription lifecycle all exist with no dead ends. Minor gaps remain (e.g. no dedicated trade time-series other than the US macro dashboard, and no direct tool-listing aside from discover_tools), but agents can work around them.