get_partiesList all political parties ("partier") known to Stortinget. Returns id (e.g. "A", "H", "FrP"), navn (full name), and representert_parti (currently represented).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Input schema / examplesAdded value: +[
+ {}
+]Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already establish read-only, idempotent, non-destructive behavior. The description adds useful context by defining what the returned fields mean (e.g., 'currently represented') and providing example IDs, going 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?
The description is two short sentences, front-loaded with the action, and includes just enough detail (examples and field explanations) without extraneous text.
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 zero-parameter list tool, the description fully covers purpose, scope, and return fields. The absence of an output schema is compensated by describing the fields. No critical context is missing.
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 tool takes no parameters, so schema coverage is complete. The description adds value by explaining the semantics of the output fields, which is helpful for interpreting results.
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 all political parties known to Stortinget and specifies the returned fields (id, navn, representert_parti). This distinguishes it from sibling tools like get_representatives that focus on people rather than parties.
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 the tool is for retrieving the full list of parties, but it does not explicitly compare with alternatives or state when not to use it. Since the purpose is so specific, usage is inferred, but no exclusion criteria are given.
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.