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, and destructiveHint=false, so safety is covered. The description adds meaningful behavioral context beyond annotations: return fields are enumerated (id, type, params, created_at, last_fired_at, fire_count), which is critical given there is no output schema, and it discloses the active-only default scoping.
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 with zero waste: the first states action, scope, and return format; the second states when to use it. Every clause earns its place, and the core purpose is front-loaded before the usage guidance.
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 simple read-only list tool with no required parameters and strong annotations, the description is complete. It covers what is returned (compensating for the missing output schema), the active-only default, and both primary use cases. The large sibling set raises the differentiation bar, and the description clears it for the closely related subscribe/unsubscribe tools.
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% for the single include_inactive parameter, so the baseline is 3. The description adds complementary value by framing 'active subscriptions' as the default, reinforcing the parameter's default-false semantics, and by noting the returned 'id' is what you'd use to cancel — connecting the output to the unsubscribe workflow.
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 states a specific verb and resource — 'List the caller's active subscriptions' — and distinguishes itself from the sibling tools subscribe and unsubscribe by framing this as a review/lookup operation. The caller scoping ('the caller's') removes any ambiguity about which subscriptions are returned.
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 second sentence gives explicit when-to-use guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This maps directly to the sibling tools subscribe ('before adding more') and unsubscribe ('find an id to cancel'), routing the agent to the correct alternative without ambiguity.
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 have unclear or overlapping boundaries: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, while ask_pipeworx, deep_research, and validate_claim all handle factual lookup/research tasks. The five polymarket_* tools plus bet_research also overlap enough that an agent could easily pick the wrong entry point despite verbose descriptions.
All names are snake_case and generally descriptive, but conventions are mixed: some are verb-first (ask_pipeworx, resolve_entity, validate_claim), some are noun-first (abn_lookup, entity_profile, polymarket_edges), and prefixes like pipeworx_ and polymarket_ are used inconsistently. It is readable but not a clean, predictable pattern.
34 tools is far too many for a server named 'Abn Lookup' — most of the surface is a broad data-research platform with prediction-market analysis, memory, subscriptions, feedback, and web utilities. The count could fit a large platform, but under this server name and with several near-duplicate entry points, it feels bloated.
For a read-only lookup/research server, coverage is strong: ABR lookups, entity resolution, single-query research, grounded verification, deep research, company profiles, comparisons, change feeds, prediction-market analysis, memory, and subscriptions are all represented. Minor gaps exist (e.g., no ACN search-by-name, no order execution), but no core workflow dead-ends.