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 cover readOnlyHint and destructiveHint. The description adds behavioral context: returns specific fields, defaults to active subscriptions, and explains the include_inactive parameter. 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?
The description is two short sentences with no extraneous information. Every sentence adds value: first explains what the tool does, second gives usage advice.
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 tool with one optional parameter and no output schema, the description is complete. It covers purpose, return fields, default behavior, and usage context. Annotations handle safety.
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 parameter's description in the schema is clear. The tool description does not add further meaning beyond the schema, so 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 it lists the caller's active subscriptions and specifies the returned fields (id, type, params, etc.). It distinguishes itself from sibling tools like subscribe and unsubscribe by focusing on listing existing subscriptions.
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 provides explicit guidance: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' While it doesn't list alternatives, it gives clear when-to-use context.
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_beta is explicitly identical to ask_pipeworx, and the multiple Polymarket tools (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk) all surface betting opportunities with only subtle differences. The line between ask_pipeworx, ask_pipeworx_grounded, deep_research, and discover_tools is also fuzzy, making misselection likely.
All tool names use snake_case and are readable, but naming conventions are mixed: many follow verb_noun (ask_pipeworx, discover_tools, list_foreign_principals), while others use noun-first or adjective_noun (entity_profile, polymarket_arbitrage, recent_alerts). Several tools share the 'pipeworx_' or 'polymarket_' prefix without that prefix meaning a consistent action type.
34 tools is high for a single MCP server, and the scope spans unrelated domains (general data lookup, prediction-market analytics, FARA registrations, memory, subscriptions, AI-visibility monitoring). This feels like several servers merged rather than one cohesive set; many tools could be split into focused modules without losing functionality.
Core workflows are well covered: flexible data querying (ask_pipeworx, grounded, deep_research), entity resolution, FARA search/document retrieval, memory CRUD, subscription lifecycle, and claim validation. There are minor gaps such as no direct tool for single-source parameterized queries (everything routes through the universal router) and no account/profile management, but agents can complete the main advertised tasks.