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, and destructiveHint false, indicating a safe read operation. The description adds context about default behavior (active subscriptions only) and the specific fields returned, which is valuable beyond the 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?
The description is two sentences long, with the first sentence stating the action and return fields, and the second providing usage guidance. No redundant information, perfectly front-loaded and concise.
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 list tool with one optional boolean parameter and no output schema, the description fully covers what is needed: it states what the tool returns, the default scope, and when to use it. No gaps remain.
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% with one parameter (include_inactive) fully described. The description does not add additional semantics about this parameter beyond implying the default (active only). Baseline score of 3 is appropriate as the schema already handles parameter documentation.
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 resource 'the caller's active subscriptions'. It distinguishes from sibling tools like subscribe and unsubscribe by focusing on listing existing subscriptions. It also specifies the fields returned, leaving no ambiguity about what the tool does.
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.' This provides clear context for when to use the tool. While it does not explicitly state when not to use it, the guidance is sufficient given the presence of sibling tools for subscribing and unsubscribing.
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 overlap in purpose, such as multiple query tools (ask_pipeworx, ask_pipeworx_grounded, deep_research) and multiple prediction market analysis tools (bet_research, polymarket_arbitrage, polymarket_edges). An agent would struggle to select the appropriate tool due to ambiguous distinctions.
Tool names are a mix of styles: some follow verb_noun (ask_pipeworx, forget), others use noun_verb (ecfs_docket_filings), and many are short or compound (bet_research, entity_profile). While the ecfs- prefix tools are consistent, the overall set lacks a uniform pattern.
With 35 tools, the count is excessive for a server named after FCC ECFS, as only 4 tools are directly relevant to that domain. The inclusion of many general-purpose data tools makes the set feel bloated and misaligned with the server's apparent scope.
For the FCC ECFS domain, the tool set is incomplete (only 4 tools, lacking submission or deletion capabilities). However, as a general-purpose data query server, it covers many sources (SEC, FDA, patents, etc.). The name mismatch hurts the perceived completeness.