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 safety is covered. The description adds meaningful context by specifying the scope ('caller's active subscriptions') and the returned fields, which informs the agent about what to expect without over-explaining.
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: the first delivers the core purpose, the second lists return fields and practical use cases. Every sentence earns its place, and there is no wasted text or repetition of schema details.
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 sufficiently complete. It explains what is returned, the scope (active subscriptions), and why an agent would call it, making it self-contained for correct usage.
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 include_inactive is already well described in the schema. The description does not add parameter-level detail but does list return fields, providing some value beyond the 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 a specific action ('List the caller's active subscriptions') and identifies the resource. It also distinguishes itself from sibling tools like subscribe and unsubscribe by framing the purpose as reviewing existing monitoring before adding more or finding an id to cancel.
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 explicit use cases: review what you're monitoring before adding more, or find an id to cancel. It does not explicitly state when not to use it or name alternative tools, but the context is clear enough for an agent to decide.
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.
The tool list mixes a small ChEMBL dataset with a large Pipeworx toolkit, and several Pipeworx tools are near-duplicates (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded; polymarket_arbitrage, polymarket_edges, polymarket_fill_risk). An agent would struggle to pick the right tool among overlapping prediction-market and research tools, and the ChEMBL tools are buried under irrelevant functionality.
Naming conventions are mixed: ChEMBL tools use bare nouns (molecule, target, activities) while Pipeworx tools use inconsistent verb_noun phrases (ask_pipeworx, validate_claim) and noun phrases (entity_profile, recent_changes). There is no predictable pattern across the set.
37 tools is far too many for a server named 'Chembl', especially since the majority are unrelated Pipeworx features. The count is justified neither by the apparent ChEMBL scope nor by a coherent overall purpose, making the server feel bloated and unfocused.
The ChEMBL subset is reasonably complete (search, molecule, target, activities, mechanism, drug_indications), and the Pipeworx side includes broad research/data tools, but the set lacks a unified purpose. Gaps include no direct assay/detail retrieval and no coherent lifecycle across the mixed domains.