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 and idempotentHint. The description adds that it lists active subscriptions by default and mentions the returned fields, which is consistent and slightly extends transparency. No contradictions.
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, no unnecessary words. Purpose and usage are front-loaded. Every sentence adds value.
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 pagination parameters, the description covers purpose, output fields, and parameter behavior. Lacks details on sorting or limits, but this is acceptable given the tool's simplicity.
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 parameter include_inactive, and its description explains usage. The tool description adds context about default behavior, but this is already implied by the schema. Baseline 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', resource 'subscriptions', and scope 'caller's active', which distinguishes it from sibling tools like subscribe and unsubscribe. The returned fields are listed, providing specific value.
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 states when to use this tool: 'to review what you're monitoring before adding more or to find an id to cancel.' This gives clear context and implies alternatives (subscribe, unsubscribe).
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 heavily overlapping purposes: ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research/validate_claim/ask_pipeworx all route factual questions to the same underlying catalog, and ai_visibility_check is essentially wrapped by scan_competitor_ai_presence. The long descriptions help, but the boundaries between query, research, and verification tools are genuinely ambiguous.
Names are all lowercase snake_case, but there is no consistent verb_noun or domain pattern: ask_pipeworx, findymail_find_email, scan_competitor_ai_presence, polymarket_kalshi_spread, and generate_llms_txt each use a different structural convention. The mix of brand prefixes, domain prefixes, and bare commands makes the naming feel ad hoc rather than systematic.
33 tools is well over the 25+ threshold and reflects a server that bundles at least five distinct concerns: email lookup, structured data research, prediction-market analysis, subscriptions, and memory. Most individual tools earn their place, but the count is too high for coherent tool selection in a single MCP server.
Coverage is broad and mostly self-sufficient for the Pipeworx data ecosystem: querying, deep research, entity resolution, comparisons, verification, subscriptions, memory, and one-off utilities are all present. There are notable gaps though—there is no tool to fetch a full record from a returned pipeworx:// citation URI, and the Findymail side is limited to find/reverse with no verification or bulk capability.