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 return field list and confirms active subscriptions, providing useful behavioral 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: first covers purpose and output, second gives usage guidance. No extraneous information, every sentence earns its place.
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 list tool with rich annotations and one optional parameter, the description sufficiently covers purpose, output, and usage. No gaps for correct agent invocation.
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% with one parameter fully described. Description does not mention the parameter, but schema carries the burden. Baseline score 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?
Description clearly states verb 'list', resource 'subscriptions', and scope 'caller's active'. It also lists returned fields, making purpose unambiguous. Siblings include subscribe/unsubscribe, so it effectively distinguishes.
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 says to use for reviewing monitoring before adding more or to find an id to cancel. Provides clear usage context, though it does not explicitly mention 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.
The set contains several clusters of near-overlapping tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx/deep_research/validate_claim all handle natural-language queries, and bet_research/polymarket_edges/polymarket_arbitrage scan the same prediction-market space. The descriptions are detailed, but that does not remove the boundary confusion.
Almost all tools use lowercase snake_case with recognizable patterns such as verb_noun or prefix_domain (nihr_, polymarket_, pipeworx_). There are minor deviations like ask_pipeworx_beta vs ask_pipeworx_grounded and mixed noun/verb phrasing, but the naming is predictable overall.
36 tools is beyond the typical well-scoped server size, and the set reads as several products bundled together: NIHR grants, Pipeworx data research, prediction markets, memory, subscriptions, and standalone utilities like generate_llms_txt or scan_dependency. Even for a broad data platform this is too many to navigate coherently, and it is a severe mismatch for a server named 'Nihr'.
Within its subdomains the set covers core workflows: query (ask/deep_research/validate), entity resolution/profile/comparison, NIHR grant lookup by several dimensions, prediction-market analysis through fill-risk, and memory/subscription lifecycles. But it is a collection of partial products rather than one coherent domain, and some outputs such as pipeworx:// citations or detected arbitrage opportunities lack an obvious in-set tool to consume them further.