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signal-bureau

by io.github.evolutionlabs-dev

get_desk_feed

A newsletter DESK's machine feed at one predictable, slug-addressable location: up to 14 days of the desk's observed items (title, url, source), newest-first, from the same panel the human edition reads. Unlike watch topics, the desk roster is PUBLIC PRODUCT — the desks are: cognitive-investor, tax-nexus, crc-kras, crc-digest, space-economy, paradise-valley, lyt-intel, aztc-aasd, newspace-asu, lq-listening, jedi-knowledge, ud-articles, ecomm-news — so an unknown-desk reply lists them freely. Delivery is keyed (your self-issued key, x-api-key): identified delivery is the standard shape for standing feeds. Desks are priced as labeled topic bundles at the published Watch rate — see DESK_BUNDLES on /api/tariff and get_quote for your exact basket. Each feed's payload declares its own coverage state.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
daysNoHow many recent days to return (1-14, default 14)
deskYesThe desk slug (e.g. 'space-economy')

TDQS

A4.3/5.0
Behavior4/5

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

With no annotations, the description discloses ordering ('newest-first'), time window ('up to 14 days'), item fields, keyed authentication ('x-api-key'), behavior for unknown desks, and that payload declares coverage state. It does not explicitly state read-only semantics, but the feed nature strongly implies it.

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

Conciseness4/5

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

The description is moderately long but each sentence contributes unique information: purpose, roster, delivery, pricing, and coverage state. It is front-loaded with the core function, though the desk list and pricing details add extra length.

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?

Given the lack of annotations and output schema, the description covers retrieval scope, authentication, differentiation from watch topics, and pricing. It does not detail error cases or exact response envelope, but the mention of 'payload declares its own coverage state' mitigates some uncertainty.

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 schema already covers both parameters with descriptions and examples. The description adds the full list of valid desk slugs and clarifies the days parameter as a lookback window, enriching the parameter meaning beyond the schema.

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 returns a desk's machine feed: 'up to 14 days of the desk's observed items (title, url, source), newest-first'. It also distinguishes itself from sibling tools by explicitly contrasting with 'watch topics' and naming the public desk roster.

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

Usage Guidelines4/5

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

Provides context for when to use: 'identified delivery is the standard shape for standing feeds' and contrasts with watch topics. It also directs to get_quote for pricing, but does not explicitly state 'use this instead of X' except through the watch topic differentiation.

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

A4/5.0
Disambiguation5/5

Each tool targets a distinct function: ask is async event queries, get_answer retrieves results, get_signals and top_accelerating provide different signal views, get_entity and search_entities cover entity details and search, etc. No two tools have overlapping purposes, and descriptions clearly separate them.

Naming Consistency4/5

The majority of tools follow a 'get_' prefix for retrieval (13 of 21) and other clear verbs like 'ask', 'create_order', 'propose_topic', 'send_feedback'. A few use noun phrases like 'todays_brief' and 'top_accelerating', which breaks the pattern slightly but are still intuitive. Overall consistent and predictable.

Tool Count5/5

21 tools cover the full scope of the server: exploration, querying, ordering, account management, feedback, and data records. Each tool serves a clear purpose without redundancy, and the count feels well-scoped for the domain.

Completeness4/5

The tool surface covers all major workflows: browsing signals, asking questions, getting detailed reports, ordering, providing feedback, and proposing new topics. Minor gaps exist (e.g., no explicit tool to cancel an order or manage watched topics directly), but core operations are present and agents can handle the domain effectively.

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