list_municipalitiesFind Sweden municipality/region ids by name substring. Returns id, title, and type ("K" = municipality of 290, "L" = region/landsting of 21). Use the returned id with get_data. Omit title to list all.
| Name | Required | Description | Default |
|---|---|---|---|
| title | No | Substring of the municipality/region name, e.g. "Stockholm" or "Göteborg". |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true, destructiveHint=false, idempotentHint=true, so the tool's safety profile is clear. The description adds behavioral context: it returns specific fields (id, title, type) and explains the type codes (K vs L with counts). This supplements the annotations well.
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 three sentences: first states core function, second explains return types, third gives usage tip. No extraneous words, well-structured, and front-loaded with key 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?
Given the tool's simplicity (one optional parameter, no output schema), the description fully covers what an agent needs: purpose, input, output, usage pattern, and relationship to sibling tool get_data. No gaps.
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 the schema already documents the parameter. The description adds meaning by clarifying that 'title' is a substring, and that omitting it lists all. This goes beyond the schema's basic description.
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 finds municipality/region ids by name substring, specifies the resource (Sweden municipalities/regions), and explains the return values (id, title, type). It effectively differentiates its purpose from siblings by linking to get_data.
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 tells when to use the tool (to find ids by name substring) and how to use the returned id with get_data. It also provides a key usage tip: omit title to list all. This distinguishes it from alternatives like get_data.
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 overlap heavily: ask_pipeworx, ask_pipeworx_beta (explicitly identical when no routing candidate is active), ask_pipeworx_grounded, deep_research, and validate_claim all answer factual questions through the same underlying router. The prediction-market cluster (polymarket_edges, polymarket_arbitrage, bet_research, polymarket_edge_tracker, polymarket_fill_risk, polymarket_kalshi_spread) has substantial purpose overlap that requires reading long descriptions to disambiguate.
Mostly snake_case, and the pipeworx_/polymarket_/ask_ prefixes give some structure, but conventions are mixed: some tools are verb_noun (list_municipalities, get_data), some are bare verbs (forget, recall, remember), and some are noun phrases (entity_profile, deep_research, bet_research, recent_changes). The inconsistent prefixing across meta-tools (ask_, deep_, entity_, scan_, validate_) makes the surface feel less predictable than it could be.
35 tools is on the heavy side, but the bigger problem is that the server name 'Kolada Se' matches only 4 tools (search_kpi, list_municipalities, list_org_units, get_data), while the other 31 tools belong to unrelated domains (Pipeworx data routing, prediction markets, memory, subscriptions, AI visibility). This is a severe scope mismatch that makes the count feel bloated and unfocused.
For the Kolada domain named by the server, the surface is minimal: you can search KPIs, list municipalities, list org units, and fetch single-KPI data, but there is no multi-year bulk fetch, cross-municipality comparison, or unit-level data retrieval. The broader Pipeworx/prediction-market surface is fairly feature-complete, but it is not what the server name implies, so the set as a whole leaves the apparent domain thinly covered.