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, idempotentHint, and destructiveHint. Description adds that it returns specific fields and defaults to active subscriptions, which is useful context beyond 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 without any fluff. The first states purpose and output, the second gives usage guidance. Efficient and well-structured.
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 tool with one optional param and no output schema, the description explains what is returned and when to use it. It is complete enough for an agent to use 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 coverage is 100%, and the description does not add significant new meaning about the parameter beyond what the schema provides. Baseline of 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?
Clearly states it lists active subscriptions and specifies the returned fields. It is distinct from sibling tools like subscribe/unsubscribe, though no explicit differentiation is made.
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 recommends using it before adding more subscriptions or to find an id to cancel, providing clear context. Does not mention when not to use or alternatives, but sufficient for the simple tool.
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.
The ask_pipeworx / ask_pipeworx_beta / ask_pipeworx_grounded trio is genuinely confusing — beta is explicitly identical to stable, and grounded differs only in output format, so agents will struggle to pick the right one. The six Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread, bet_research) also blur together despite distinct purposes, and ai_visibility_check vs scan_competitor_ai_presence overlap heavily.
Naming is largely consistent: snake_case throughout, with a strong verb-first pattern (list_subscriptions, resolve_entity, validate_claim, compare_entities, discover_tools) and clear domain prefixes for clusters (neso_, pipeworx_, polymarket_). The only inconsistency is the ask_pipeworx family, which differentiates by bare suffix (_beta, _grounded) rather than a descriptive verb.
35 tools is on the heavy side, but the server is a broad multi-domain data platform (SEC, FDA, FRED, NESO, Polymarket, npm, memory, subscriptions, discovery) where the count is arguably justified. Several tools could be consolidated — the three ask_pipeworx variants are redundant, and scan_competitor_ai_presence largely wraps ai_visibility_check — which would tighten the surface meaningfully.
Coverage is strong across the stated domains: lookups, deep research, entity resolution, claim verification, comparisons, memory lifecycle (remember/recall/forget), subscription lifecycle (subscribe/unsubscribe/list/recent_alerts), discovery (discover_tools, suggest_questions), feedback, and trending. Minor gaps exist (no direct document-fetch tool, scan_dependency is npm-only in v1) but nothing that creates a dead end.