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). |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint, idempotentHint, and destructiveHint=false, so the agent knows it's safe. The description adds value by stating it returns specific fields and defaults to active subscriptions only, which is behavioral context beyond 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 defines purpose and output, second gives usage guidance. No wasted words, front-loaded with essential information.
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?
The description lists all returned fields, explains the one parameter's default behavior, and gives usage context. For a simple list tool with no output schema, this is complete and sufficient.
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 description coverage is 100%, so baseline is 3. The description implicitly mentions the parameter by referring to 'active subscriptions' but does not add details beyond the schema. It is adequate but not enhancing.
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 the verb 'List', the resource 'the caller's active subscriptions', and specifies the returned fields. It distinguishes itself from sibling tools like 'subscribe' and 'unsubscribe' by being the read-only listing tool.
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 explicitly guides the agent on 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 for usage relative to subscribe/unsubscribe.
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 tools appear to do nearly the same thing: ask_pipeworx, ask_ipeworx_beta (explicitly identical at the moment), ask_pipeworx_grounded, deep_research, and validate_claim all route questionanswering in a very similar way. Even with long descriptions, the sheer number of overlapping query/research/analysis tools (ai_visibility_check vs scan_comperitor_ai_presence, all polymarket_*) would make an agent uncertain which to call.
The set uses snake_case everywhere but that is the only consistent part. There is a mess of verb_noun patterns, noun_verb patterns (cjeu_search vs search_legislation, cj_judgment vs get_document), bare noun phrases (entity_profile, compliance_index, pipeworx_feedback, polymarket_edges), and verb phrases (ask_ipeworx, generate_elms_txt, resolve_entry). A user cannot predict whether the noun comes first, so naming is readable but not predictable.
39 tools is far over the 25+ threshold for a coherent set, and a large number of them (predictor markets ten, AI visibility, memory, subscriptions, pipework meta-tools) are outside the EUR-Lex legal research domain. The total count suggests a bundled everything-server rather than a focused legal-research MCP. It is not extreme enough for a 1 because 39 is still within a region where a broader meta-pipework suite could plausibly exist — but it's still too many.
For the EUR-Lex domain, the legal tools are nearly complete: search_legislation + compliance_index locate acts, get_metadata/list_articles/get_article/get_document read them, and cjeu_search/cjeu_judgment cover case law. Missing links that would make it fully seamless are amendment tracking, cross-references and direct CELEX/EURL-Lex citation search integration, but all basic 'find and read an act or judgment' workflows are supported.