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, destructiveHint=false, idempotentHint=true. Description adds details about return fields and default behavior (active only), which is consistent and provides additional transparency beyond annotations.
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 action and output, no wasted words – highly efficient.
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?
The tool is simple with one optional parameter and no output schema. Description lists return fields and gives use case, which is sufficient for an agent to invoke correctly.
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 already describes the only parameter (include_inactive) fully (100% coverage). Description does not add new parameter-level information, so 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?
Description clearly states 'List the caller's active subscriptions' and specifies returned fields, distinguishing it 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 says to use this 'to review what you're monitoring before adding more or to find an id to cancel', providing clear context for when it should be used.
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.
Many tools have distinct purposes, but there are overlapping functions (e.g., ask_pipeworx vs ask_pipeworx_grounded, entity_profile vs compare_entities vs recent_changes) that could confuse an agent. However, detailed descriptions help mitigate ambiguity.
Tool names follow no consistent pattern: snake_case (ai_visibility_check), camelCase (none here but inconsistent), and mixed verb/noun styles (subscribe vs entity_profile). The naming is chaotic and does not follow a predictable convention.
With 33 tools, the server is bloated for its claimed Gistemp focus (only 3 tools directly about Gistemp). Most tools belong to a broader Pipeworx platform, making the scope too broad and unfocused.
For a server named 'Gistemp,' the tool set is severely incomplete—only 3 tools cover temperature anomalies. The majority of tools address unrelated domains (Polymarket, SEC filings, memory, etc.), leaving obvious gaps for the stated purpose.