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pronto-stream

by io.github.nmxmxh
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search_signals

Read-onlyIdempotent

Find signals by the words in them. Use this when you are looking for a subject — a storm name, a company, a phrase a report would print — and cannot name it as a category. When you can name it as a category instead (a country, a domain, a severity), scope_signals selects that slice exactly and does not depend on any particular word appearing; this tool ranks by word overlap and will miss a matching signal that phrased it differently.

The search covers places, observations, summaries, identifiers, and the country and topic facets of each signal, so "Japan" reaches a Japanese-language article that never writes the word.

A signal is returned when it contains the words you asked for. Inflections count: "flood" reaches "flooding" and "quake" reaches "quakes". Synonyms do not: the match is lexical, not semantic, so "car" does not reach "automobile" and "downturn" does not reach "recession". Use the words the source would have used.

Results are ranked by how much of your query each signal contains, exact phrase matches first. When nothing contains your terms the tool returns an error rather than the closest available rows; an empty result means the wire does not carry it, not that the search gave up. Returns CWF lines.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNorelevance (default): word coverage, exact phrase first. significance: the server's composed attention score, which lifts severity and connectivity over freshness — use it when looking for consequence rather than currency.
limitNoMaximum matches to return (default 10, max 50)
queryYesKeywords describing the anomaly or topic, e.g. 'earthquake pacific' or 'solar flare'
cursorNoPage token from META cursor: of the previous response.
localeNoOptional BCP-47 language code (e.g. 'ja', 'zh', 'de', 'ar'). When set, response metadata is returned in the requested language context.
min_severityNoDrop results below this published band. Unrated rows are dropped too, because absence of a rating is not evidence of safety.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changed
    • addedInput schema / properties / locale
      Added value: +{
+  "description": "Optional BCP-47 language code (e.g. 'ja', 'zh', 'de', 'ar'). When set, response metadata is returned in the requested language context.",
+  "type": "string"
+}
  2. Changed3 schema fields changed
    • addedInput schema / properties / cursor
      Added value: +{
+  "description": "Page token from META cursor: of the previous response.",
+  "type": "string"
+}
    • addedInput schema / properties / min_severity
      Added value: +{
+  "description": "Drop results below this published band. Unrated rows are dropped too, because absence of a rating is not evidence of safety.",
+  "enum": [
+    "info",
+    "low",
+    "medium",
+    "high",
+    "critical"
+  ],
+  "type": "string"
+}
    • addedInput schema / properties / sort
      Added value: +{
+  "description": "relevance (default): word coverage, exact phrase first. significance: the server's composed attention score, which lifts severity and connectivity over freshness — use it when looking for consequence rather than currency.",
+  "enum": [
+    "relevance",
+    "significance"
+  ],
+  "type": "string"
+}
  3. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The description goes well beyond the readOnly/openWorld/idempotent annotations by disclosing match semantics (inflections count, synonyms don't), ranking order (exact phrase first, word coverage), error behavior (returns error rather than closest rows), and return format ('Returns CWF lines'). This is rich behavioral context the agent needs to interpret results correctly.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose and usage rule, then organized into focused paragraphs on coverage, matching semantics, and result behavior. Each sentence adds distinct value with no filler.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 6-parameter search tool with no output schema, the description covers usage, alternative selection, matching behavior, ranking, error handling, and return format. The domain-specific 'CWF lines' is a minor shorthand but the behavioral guidance is otherwise comprehensive.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the baseline is 3. The description adds interpretive meaning for the query parameter (matching behavior across inflections, no synonyms) and for sort (ranking details not fully in the schema), elevating it above baseline.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb and resource: 'Find signals by the words in them.' It explicitly names the sibling scope_signals as the alternative for category-based lookup, so an agent can differentiate the tools without opening schemas.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use: 'Use this when you are looking for a subject — a storm name, a company, a phrase a report would print — and cannot name it as a category.' It also names the alternative and the condition that selects it, explaining the lexical-match limitation that matters for tool choice.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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