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Pronto

by stream.pronto.api

get_facet_manifest

Read-onlyIdempotent

Read the shape of the wire in one small response: a two-level tree of domain and the topics under it, each with a signal count, ranked so the busiest lead. This is the map to read first — read it once, choose a scope offline, then make one precise call to scope_signals or search_signals instead of guessing a filter.

It covers two facet keys only, domain and topic, and truncates the topic tail under each domain, so it stays short enough to read in full. When you need the rest of the vocabulary — the languages, countries, providers, severities, coverages and place ids you can also filter by, exhaustively and with counts — call list_facets instead. The manifest states structure, not signal content.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

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

Annotations already flag read-only, idempotent, and open-world behavior. The description adds genuinely useful behavioral context: response is intentionally small, topic tails are truncated, only two facet keys are covered, and the result is structure rather than signal content.

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?

Front-loaded with the core output shape before adding positioning and exclusions. Every sentence provides actionable guidance; there is 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 zero-parameter, no-output-schema tool, the description gives enough to know what the response contains, how it is sorted, what is truncated, and which alternative to use when broader facets are needed.

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?

The tool has zero parameters, so the baseline is 4 and there is little to document. The description does not need to explain input semantics because no inputs exist.

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 clearly states the tool reads a facet manifest: a two-level tree of domain and topic with signal counts, ranked by busiest. It also differentiates itself from siblings by noting it covers only domain and topic, whereas list_facets covers the full vocabulary.

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?

Explicit when-to-use guidance is provided: read this map first, choose a scope offline, then make a precise call to scope_signals or search_signals. It also names the alternative for exhaustive vocabulary, list_facets, and states what the manifest does not contain.

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

A4.6/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: get_signal re-reads a known row, get_latest_signals fetches by time, scope_signals filters by facets, search_signals matches words, and get_related_signals follows links. The overlapping pairs like get_facet_manifest/list_facets and get_fused_signal/list_fusion_products are explicitly differentiated in their descriptions, so an agent should not confuse them.

Naming Consistency5/5

The naming follows a consistent snake_case verb_noun pattern: get_ for direct fetches, list_ for catalog-style enumeration, register_ for identity creation, and scope_/search_ for query actions. The slight difference between get_fused_signal and list_fusion_products is meaningful and the verbs remain predictable.

Tool Count5/5

Thirteen tools is well within the sweet spot and each one covers a distinct capability: live reads, lookup by id, lexical search, facet filtering, related signals, fused products, catalogues, plans, billing, and agent registration. There is no obvious padding or excessive fragmentation.

Completeness5/5

The surface fully covers the domain: discovering the vocabulary, selecting signals, searching, fetching by id, following relationships, computing derived products, listing sources, and checking billing/plans. The only gaps would be account claiming and credential rotation, but those are explicitly deferred to external parties, so they are not tool-set gaps.

Resources