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, so the description is not required to restate those. It goes beyond annotations by clarifying the scope ('caller's active subscriptions') and explicitly listing the returned fields, which informs the agent about the output shape. It does not mention pagination or potential volume, but this is a minor gap given 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: the first states the action and scope, the second lists return fields and provides usage guidance. Every sentence serves a purpose with no filler, and the most critical information is front-loaded.
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 list tool with one optional parameter, the description covers the function, output fields, and usage context. Combined with strong annotations and full schema coverage, it is complete enough for an agent to select and invoke the tool 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 coverage is 100% and the only parameter, include_inactive, has a clear description in the schema. The tool description does not add extra meaning for this parameter, so 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 a specific verb ('List') and resource ('the caller's active subscriptions'), clearly distinguishing it from sibling tools like subscribe and unsubscribe. It also enumerates the returned fields, leaving no ambiguity about the tool's function.
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?
It explicitly states when to use the tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides direct context and contrasts with the actions of adding (subscribe) and cancelling (unsubscribe), serving as clear guidance among alternatives.
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 tool families heavily overlap: ask_pipeworx, ask_pipeworx_beta (explicitly identical), ask_pipeworx_grounded, deep_research, and validate_claim all answer natural-language questions, while polymarket_arbitrage, polymarket_edges, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread, and bet_research all analyze prediction markets. An agent would struggle to pick the correct tool without reading very long descriptions.
All names are snake_case and readable, but the style is inconsistent: some are bare nouns (sequence, variation, homology), some are single verbs (lookup, recall, forget), and others are long descriptive phrases (scan_competitor_ai_presence, polymarket_kalshi_spread). There is no consistent verb_noun or resource_noun pattern across the set.
38 tools is excessive for a coherent server, and nearly all of them are unrelated to the server's stated name ('Ensembl') — only about 7 tools (lookup, lookup_symbol, sequence, variation, vep, xrefs, homology) actually belong to the Ensembl domain. The rest form several unrelated clusters (Pipeworx data queries, prediction markets, memory, subscriptions), making the tool count feel bloated and unfocused.
For an Ensembl server, the surface is thin: it covers ID lookup, sequence retrieval, variants, VEP, xrefs, and homology, but omits other core Ensembl functionality such as gene trees, alignments, regulation, expression, and assembly data. Meanwhile the many non-Ensembl tools don't form a complete domain of their own — they are a grab bag of unrelated utilities.