get_casesList parliamentary cases/bills ("saker") for a session. Returns saker_liste with numeric case ids (sakid), titles, topics (emne_liste) and status.
| Name | Required | Description | Default |
|---|---|---|---|
| sesjonid | Yes | Session id, e.g. "2023-2024". Get valid ids from get_sessions. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "sesjonid": "2023-2024"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description adds concrete behavioral details beyond the annotations, specifying the return structure (saker_liste with sakid, titles, emne_liste, and status) and the session scope. This complements the read-only and idempotent hints with practical information.
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?
The description is two sentences, front-loaded with the action and resource, and every word contributes meaning. No redundant phrases or filler.
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 single-parameter list tool with full schema descriptions and safety annotations, the description sufficiently covers the purpose, scope, and return format. No output schema exists, but the description fills that gap adequately.
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 the sesjonid parameter fully described including an example and cross-reference to get_sessions. The tool description adds no new parameter information beyond 'for a session,' so it meets the baseline but provides no extra value.
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 tool lists parliamentary cases/bills ('saker') for a session, with a specific verb and resource. It distinguishes itself from sibling tools like get_votes and get_sessions by naming the exact resource type and session context.
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 implies usage for retrieving cases by session, and the parameter description instructs users to get valid session IDs from get_sessions. However, it does not explicitly exclude other tools or provide when-not-to-use guidance, though the context is clear.
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 near-duplicates: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded all route questions to the same underlying toolset with only minor differences. Additionally, the five polymarket_* tools overlap significantly in purpose, and the mix of Storting parliament tools with a general-purpose data platform creates confusion about which tool is appropriate.
Tool names use snake_case but with inconsistent conventions. Some are verb-first (get_, list_, discover_, validate_), while others are noun-first (entity_profile, bet_research, recent_alerts). There are predictable prefixes like ask_pipeworx and polymarket_, but overall the naming pattern is not uniform, making it harder to predict tool names.
With 38 tools, the count exceeds the 25-tool threshold for 'too many.' Many tools are unrelated to the server name 'Storting No' (which implies a Norwegian parliament focus), and the broad range of data-research and prediction-market tools feels bloated for the apparent scope. A smaller, more focused set would improve coherence.
The Storting-related tools cover the main parliamentary entities (cases, parties, representatives, sessions, votes) and include an export fallback for any additional data.stortinget.no resource. The Pipeworx side has meta-tools (ask, discover, suggest) and specialized analyses (entity_profile, validate_claim, polymarket_*). However, there are notable gaps, such as no direct tool for searching parliamentary speeches or committee documents without relying on the generic export, and the mixed domains leave some workflows incomplete.