Get the scenario matrix with probabilities for a published future-space. Use when the user asks how a domain (banking, energy, CEE markets, ...) might play out, what the plausible futures are, or wants to reason across scenarios instead of a single forecast.
| Name | Required | Description | Default |
|---|---|---|---|
| slug | Yes | future-space slug from list_public_spaces |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It does not disclose any behavioral traits such as read-only nature, destructive potential, auth needs, rate limits, or error handling. The description only covers purpose and usage, not behavior beyond that.
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 clearly states purpose, second gives usage guidance. No redundant or filler content. Front-loaded with key action.
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 param and no output schema, the description covers purpose and usage adequately. However, it lacks behavioral transparency (e.g., return format, safety) and does not describe output structure, which is absent in schema. With no annotations, this is a gap.
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 'slug' parameter. The description does not add any extra meaning beyond implying the slug comes from 'list_public_spaces', which aligns with the schema. Baseline score 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 explicitly states the tool retrieves a 'scenario matrix with probabilities for a published future-space', with a clear verb ('Get') and resource. It distinguishes from siblings like 'detect_tensions' and 'extract_assumptions' by specifying the use case for scenario reasoning.
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?
Provides explicit when-to-use guidance: 'when the user asks how a domain might play out, what the plausible futures are, or wants to reason across scenarios instead of a single forecast.' It implies context but does not explicitly state when not to use or list alternatives, though the sibling list helps.
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.
Each tool targets a distinct function: text analysis (detect_tensions, extract_assumptions), retrieval of published foresight (get_public_report, etc.), listing available resources (list_countries, list_public_spaces), searching (search_spaces), and macro claim resolution (resolve_country_claim). Even similar-sounding tools like detect_tensions and get_public_tensions are clearly differentiated by purpose (user-provided text vs. published analysis).
All tool names follow a consistent verb_noun pattern with underscores: detect_tensions, extract_assumptions, get_country_indicator, get_public_report, get_public_scenarios, get_public_tensions, list_countries, list_public_spaces, resolve_country_claim, search_spaces. The verbs are specific and the nouns are descriptive, making the naming predictable and easy to understand.
With 10 tools, the server covers its domain—strategic foresight and macro indicators—without being overwhelming. Each tool serves a clear purpose, and the count is appropriate for the scope: text analysis, resource listing, retrieval, search, and claim resolution. There is no unnecessary bloat or deficiency.
The tool set covers the core workflows: analyzing user-provided strategy text, retrieving published foresight reports/scenarios/tensions, listing available spaces and countries, searching, and resolving macro claims. A minor gap is the lack of a tool to retrieve a specific extracted assumption (though extraction is stateless by design). Overall, the surface is largely complete for a public-facing server.
