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pronto-stream

by io.github.nmxmxh

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.
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. Dates show when Glama detected each change.

  1. 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"
+}
  2. First observed

TDQS

A4.8/5.0
Behavior5/5

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

Annotations already mark it read-only, open-world, and idempotent, but the description adds substantial behavior beyond that: which fields are searched, inflection matching, the lack of synonym matching, minimum severity behavior, and the custom relevance ranking. It also does not contradict the provided annotations.

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?

Each paragraph has a single job: usage and alternative, field coverage, and matching/ranking behavior. The main action and decision rule are front-loaded, and the text is dense with useful detail and no filler.

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

Completeness4/5

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

For a 5-parameter tool with no output schema, this description is unusually complete: it explains scope, matching algorithm, ranking behavior, and severity handling. It does not spell out the shape of the return object, but the parameter schema already covers limit/cursor details and the result can clearly be inferred as a list of matching signals.

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 strong. The description still adds meaningful value by explaining what the query actually matches against, that matching is lexical and not semantic, and that inflection counts but synonyms do not. This helps the agent craft a better keyword and severity context beyond what each schema field alone states.

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 opens with a specific verb and resource: 'Find signals by the words in them', then elaborates with concrete subjects like a storm name, company, or printed phrase. It also directly distinguishes this tool from scope_signals, so an agent can choose between them from the description alone.

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?

It gives an explicit decision rule: use word-based search when a subject cannot be named as a category, and use scope_signals when it can name country, domain, or severity as a category. It also warns that ranked word-overlap search can miss differently phrased matches, which sets accurate expectations for when this tool should be chosen.

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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TDQS

A4.7/5.0
Disambiguation5/5

Each tool targets a distinct operation: the two facet-listing tools are explicitly differentiated as quick manifest vs exhaustive listing, and search_signals vs scope_signals is cleanly split between lexical search and facet filtering. The billing/payment tools also form a clear lifecycle with no overlapping responsibilities.

Naming Consistency5/5

All tools follow a consistent verb_noun snake_case pattern: get_* and list_* for reads, and action verbs like register_agent, open_payment, settle_payment, scope_signals, search_signals for operations. No mixed casing or inconsistent verb styles.

Tool Count5/5

15 tools is at the upper edge of the well-scoped range but each tool earns its place: five cover data retrieval, four cover discovery/metadata, and five cover identity/billing. No redundant or filler tools.

Completeness5/5

The surface covers the full workflow for the stated domain: discover vocabulary, search/scope the live wire, retrieve individual signals and related signals, compute fused products, register an identity, and purchase/activate a plan. The only apparent omissions, such as cancellation, are outside the described prepaid self-service flow.

Resources