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 declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=true. Description adds value by specifying default behavior (active only) and the specific fields returned, which is useful beyond 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?
Two sentences: first clearly states action and return fields, second provides usage guidance. No unnecessary words, front-loaded with core purpose.
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 and no output schema, the description covers purpose, returned fields, and usage adequately. Annotations provide safety profile, making this complete.
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 parameter 'include_inactive' with a description. The tool description implies the default behavior (active only) but does not add additional syntax or format details beyond what the schema already provides, so baseline 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 the tool lists the caller's active subscriptions, specifies returned fields (id, type, params, created_at, last_fired_at, fire_count), and distinguishes from sibling tools like subscribe and unsubscribe by focusing on read-only listing.
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?
Explicitly gives two use cases: review before adding more subscriptions and find an id to cancel. Provides clear context, though does not explicitly mention when not to use or list alternatives beyond the implied counterparts.
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 form near-overlapping clusters: ask_pipeworx and ask_pipeworx_beta are explicitly identical in behavior, while ask_pipeworx, ask_pipeworx_grounded, and deep_research have overlapping scopes. The five polymarket tools also share a common prediction-market domain and could be confused despite detailed descriptions. Some boundaries between 'meta' tools such as discover_tools, suggest_questions, and ask_pipeworx are also fuzzy.
The majority of tools use lowercase snake_case and many follow a verb_noun pattern (ask_pipexors, search_within, generate_llms_txt, destroy_tools), which is readable. However, there are inconsistent orderings like ai_visibility_check and dk_tender_search, plus several one-word verb tools (remember, forget, recall) alongside noun_verb forms. So the style is mostly regular but does not follow a single consistent pattern.
34 tools is very ot heavy for a general-purpose data platform, but the server name 'Udbud Dk' implies a narrow Danish tender scope. Only 3 of the 34 tools actually concern Danish procurement, while the rest form a broad question-answering, prediction-market, subscription, and memory platform. The count feels bloated and mismatched relative to the apparent server focus.
For the broad data domain, the server covers a wide range: lookup, grounded answers, entity resolution, fact-checking, company profiles, comparisons, change feeds, polymarket opportunities, subscriptions, and memory. But relative to the Danish tender scope implied by the server name, only search/detail/recent exist and missing functionality such as saved searches or notifications for new notices. There are also some odd gaps such as no-direct citation-lookup tool, but the generous question-answering tools compensate.