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 indicate readOnly, idempotent, non-destructive. Description adds value by detailing return fields and default behavior (active subscriptions), but 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?
Two sentences, front-loaded with purpose, no redundant information. Every sentence earns its place.
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?
Tool is simple with one optional parameter and no output schema. Description compensates by listing return fields and usage context, making it fully complete for an agent to use 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% with one parameter fully described. Description does not add additional parameter semantics beyond the schema, 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', specifying verb, resource, and scope. It also lists return 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?
Description explicitly says when to use: 'review what you're monitoring before adding more or to find an id to cancel'. It does not mention when not to use or alternatives, but the guidance is clear and helpful.
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 have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route to the same 5,440 tools, with the beta currently identical to the stable version. Additionally, discover_tools and suggest_questions both serve as capability discovery entry points, and the five polymarket_* tools share similar prefix and some functional overlap. Generic names like get and search further blur boundaries, especially when 'get' could be mistaken for a generic fetch rather than a UniProt accession lookup.
Tool names mix single-word verbs (get, search, keyword), noun compounds (feature_summary, entity_profile), and prefixed families (ask_pipeworx_*, polymarket_*, scan_*). While most names use snake_case, the verb-noun pattern is inconsistent: some are action-first (ask_pipeworx, resolve_entity) and others are object-first (proteomes_search, taxonomy_search). There is no uniform convention, making it hard to predict tool names.
The server is named 'Uniprot' but only 7 of 37 tools actually relate to UniProt protein data; the rest are a broad Pipeworx data platform covering prediction markets, AI visibility, memory, subscriptions, and more. This is an extreme mismatch between the stated product and the tool surface, far exceeding the expected scope for a protein database server.
For the apparent UniProt purpose, the surface has significant gaps: no batch retrieval, no ID mapping from gene names or other databases, no sequence alignment or BLAST, and no access to UniRef/UniParc. The Pipeworx tools, while extensive via the ask_pipeworx router, still lack dedicated tools for many advertised data categories and are not comprehensive for a standalone data platform. The overall surface feels incomplete for any single coherent domain.