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 readOnly, idempotent, non-destructive. Description adds return field details and optional parameter behavior, which is valuable given no output schema.
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, front-loaded with purpose, no wasted words. 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?
Simple tool with one optional parameter and no output schema. Description covers purpose, return fields, and usage guidance adequately.
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 meaning beyond the schema's definition of include_inactive. 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?
The description clearly states the tool lists the caller's active subscriptions and specifies the returned fields (id, type, etc.). It distinguishes from siblings like subscribe/unsubscribe.
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?
Explicit guidance on when to use: 'review what you're monitoring before adding more or to find an id to cancel.' Lacks explicit when-not-to-use, but context is clear.
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.
Multiple research/query entry points overlap heavily: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded and deep_research sit on the same routing core, and validate_claim/bet_research/entity_profile all wrap lookup-and-analyze behavior. The detailed descriptions help within specialized clusters, but the central ask_pipeworx family alone creates real selection ambiguity.
All names are lower_snake_case and several families are consistent (ask_pipeworx_*, polymarket_*, remember/recall/forget), but the overall set mixes bare verbs, nouns, and verb_noun composites with no global pattern (disease, metadata, query, entity_profile, generate_llms_txt, validate_claim). It is readable but not predictable across the full 34-tool surface.
34 tools is over the 25+ threshold and the set bundles several distinct domains—disease ontology, Pipeworx data access, prediction markets, AI visibility, npm scanning, memory, and subscriptions—into one server. Each subfamily may be justified, but the combined surface is heavy and makes tool selection harder than the underlying tasks require.
The disease domain has query/disease/metadata for search-and-fetch read coverage, and the broader research side has lookup, grounded verification, deep research, entity profiles, comparisons, subscriptions, and memory lifecycle tools. Minor gaps exist (no direct tool to fetch pipeworx:// citation URIs, no disease browsing/pagination), but these are workable rather than blocking.