get_vote_resultPer-MP results ("voteringsresultat") for one vote: how each representative voted (for/against/absent). Pass a votering id from get_votes.
| Name | Required | Description | Default |
|---|---|---|---|
| voteringid | Yes | Numeric vote id (voteringid) from get_votes. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {
+ "voteringid": "5678"
+ }
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already mark the tool as read-only, idempotent, and non-destructive. The description adds behavioral context by explaining the granularity of the result (per representative) and the possible vote options (for/against/absent), which is useful given the lack of an output schema.
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 long, front-loaded with the core purpose, and contains no redundant or unnecessary information. Every word adds value.
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 tool with one parameter and comprehensive annotations, the description adequately conveys what results the agent will receive. It does not specify the exact response structure (e.g., list vs. object) but is sufficient for the tool's low complexity.
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?
The input schema fully documents the parameter 'voteringid' as a numeric vote id from get_votes. The description reiterates this but adds no extra semantic nuance beyond the schema's description, so the 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?
The description clearly states the tool's function: retrieving per-MP results for a single vote, specifying the exact data (for/against/absent). It distinguishes itself from the sibling get_votes tool, which presumably lists votes, by focusing on one vote's breakdown.
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 instructs the agent to pass a votering id from get_votes, establishing a clear prerequisite and chaining with the parent tool. However, it does not explicitly mention when not to use this tool or present alternative tools for similar needs.
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.