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 and destructiveHint=false, so the description adds value by specifying default scope (active subscriptions) and the fields returned. This goes beyond the annotations without contradicting them.
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?
The description is two concise sentences with no wasted words. The first sentence front-loads the action and output details, the second provides 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 tool with one optional parameter and no output schema, the description covers the return fields and usage context. Annotations cover safety, making the description complete.
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 any extra meaning beyond what the schema provides for the optional 'include_inactive' parameter. 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 verb 'List' and the resource 'the caller's active subscriptions', and specifies the return fields (id, type, params, etc.). It distinguishes from sibling tools like 'subscribe' and 'unsubscribe' which have different purposes.
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 description explicitly says 'Use this to review what you're monitoring before adding more or to find an id to cancel', providing clear context for when to use this tool and implicitly advising when not to (for adding or canceling, use siblings).
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 tools overlap: ask_pipeworx and ask_pipeworx_beta are currently identical, and the five prediction-market tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, polymarket_edge_tracker) cover adjacent tasks that require careful reading. However, descriptions are unusually explicit about when to prefer each, and non-overlapping domains (memory, subscriptions, earthquakes, npm) are clearly separated.
All tool names are snake_case with a mostly verb-first pattern (ask_, compare_, discover_, generate_, list_, scan_, search_, validate_), making the surface predictable. Minor deviations like deep_research, entity_profile, and single-word verbs (remember, recall, forget) break the pattern slightly, but each family is internally consistent.
33 tools exceeds the 25+ threshold for 'too many,' and several could be consolidated — ask_pipeworx_beta is redundant today, and the prediction-market suite could fold into 2-3 tools. The count reflects a genuinely wide data platform with meta-tools (discover_tools, suggest_questions, ask_pipeworx) already covering discovery, so the surface feels heavy for an agent to triage.
Core workflows are well covered: querying (ask_pipeworx, grounded, deep_research), research profiles (entity_profile, compare_entities, recent_changes), input resolution (resolve_entity), fact-checking (validate_claim), memory lifecycle, and subscription lifecycle all have complete loops. Minor gaps exist, notably no tool to fetch a pipeworx:// citation URI directly (search_within expects already-fetched text), and some one-off tools like generate_llms_txt and scan_dependency feel bolted on.