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, covering the safety profile. The description adds behavioral context by listing the exact fields returned (id, type, params, created_at, last_fired_at, fire_count), which is not present in annotations. It does not contradict 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 concise: two sentences that front-load the purpose and return fields, then provide a usage note. Every word earns its place with no redundancy or filler.
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 parameter and no output schema, the description is complete. It states what the tool returns, when to use it, and the annotations cover safety. No significant gaps exist.
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?
The input schema has full description coverage for the only parameter (include_inactive) with a clear explanation. The description does not add any parameter-specific semantics, but with 100% schema coverage, the baseline 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?
The description uses the specific verb 'List' with the resource 'the caller's active subscriptions', clearly distinguishing it from sibling tools like subscribe and unsubscribe. It also enumerates the fields returned, making the tool's function unambiguous.
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 clear use cases: 'review what you're monitoring before adding more' and 'find an id to cancel'. This implies when to use the tool relative to subscribe/unsubscribe, though it does not explicitly name the alternative tools. This is strong guidance without being overly verbose.
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 overlap heavily: ask_pipeworx_beta is currently identical to ask_pipeworx, and find_related with syn/rhy options duplicates find_synonyms and find_rhymes. The six Polymarket tools and four entity-research tools also have fuzzy boundaries, so agents will struggle to reliably pick the right one.
Conventions are mixed: most tools follow verb_noun (ask_, find_, compare_, validate_) but several are bare nouns or noun phrases (entity_profile, recent_alertes, recent_changes) and others are bare verbs (remember, forget, subscribe, unsubscribe). The server name 'words' matches only five of 36 tools, adding further confusion about what to expect here.
36 tools is well into the heavy range, and the set bundles word lookups, a universal data router, six Polymarket tools, memory, subscriptions, and meta-utilities under one server. Many of these would be better split into dedicated, purpose-scoped servers.
For the dominant data-research and prediction-market scope, the surface is strong: grounded lookups, deep reseach, entity profiles, compareions, change feeds, claim verification, edge scanners, fill-risk checks, subscriptions, and memory are all covered. Minor gaps exist: taking 'words' literally there is no defintion or spelling tool, and there is no generic open-web search, but as a data-research toolkit it is quite complete.