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, destructiveHint. Description adds return fields and parameter behavior, enhancing transparency.
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 concise sentences with clear front-loading. No extraneous words.
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?
No output schema but description lists key return fields. Context signals show simple structure; description is sufficient.
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 single parameter is well-described in schema. Description adds no extra semantic info beyond schema.
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 'List the caller's active subscriptions' with a specific verb and resource. It distinguishes from sibling tools like subscribe and 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?
Explicitly advises use 'before adding more or to find an id to cancel', providing clear context. No explicit alternatives but implied by 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 serve overlapping query purposes (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research) with subtle differences that are hard to distinguish without careful reading. Similarly, prediction market tools (bet_research, polymarket_arbitrage, polymarket_edges) have overlapping scopes.
Names are highly inconsistent: verb_noun (ask_pipeworx, resolve_entity), noun_descriptive (entity_profile, polymarket_arbitrage), and simple nouns (tmy, pvgis). No clear pattern emerges, making it hard to anticipate tool names.
34 tools is borderline high for a single server. While some utility tools (remember, forget) are justified, many tools are very narrowly scoped (tmy, generate_llms_txt) and could be merged or omitted.
The server covers a wide breadth (data querying, prediction markets, solar energy, AI visibility) but feels like a collection of unrelated domains. Core operations for data querying are present, but the solar tools (monthly_radiation, pv_performance) seem orphaned from the rest.