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 readOnly, idempotent, and non-destructive behavior. The description adds valuable context: it returns only active subscriptions by default, scopes to the caller, and lists the exact fields returned. This goes beyond annotations without contradicting them.
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 the core action and resource. The first sentence states purpose and return fields; the second gives usage guidance. No wasted words or redundant repetition.
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?
Given the low complexity (one optional parameter, no output schema), the description fully covers what the agent needs: scope, return fields, and when to use it. It even compensates for the lack of an output schema by listing the return fields explicitly.
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% for the single optional parameter include_inactive, and the schema description is clear. The description doesn't explicitly discuss the parameter but implicitly references active vs. inactive subscriptions, which is consistent. Baseline 3 is appropriate since the schema does the heavy lifting.
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 identifies the resource as 'the caller's active subscriptions.' It clearly distinguishes this from siblings like subscribe and unsubscribe, and it enumerates the returned fields, 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?
It explicitly states when to use this tool: 'Use this to review what you're monitoring before adding more or to find an id to cancel.' This provides clear context and implies alternatives (subscribe/unsubscribe) for other actions.
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 are nearly indistinguishable in role: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are all variants of the same router, with the beta version explicitly noted as currently identical to the stable one. The six Polymarket tools also overlap heavily around edge-finding, arbitrage, and fill-risk analysis, making misselection likely.
Names are uniformly snake_case and groupable into prefixes like polymarket_* and pipeworx_*, but the set does not follow a consistent verb_noun convention. Noun-first names like entity_profile and recent_alerts sit alongside verb-first names like read_feed and validate_claim, and product-name suffixes such as ask_pipeworx_beta/grounded add further inconsistency.
At 34 tools, the surface is well past the 25+ threshold and far broader than the 'Crypto Feeds' name suggests. The set spans feed reading, general data research, prediction markets, AI-brand visibility audits, npm dependency scanning, memory, and subscriptions, making it feel like a platform-wide dump rather than a focused MCP server.
The broad research workflow is well covered with query, grounded answer, deep research, entity profiles, comparisons, fact-checking, and entity resolution. However, feed functionality is read-only with no feed management, subscription types do not include crypto feeds despite the server name, and there is no dedicated tool for resolving the advertised pipeworx:// citation URIs.