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signal-bureau

by io.github.evolutionlabs-dev

get_signals

START HERE for browsing — the cheap read (500/day, no model work). The full machine-readable signal feed (same payload as GET /api/signals): every entity currently flagged by the attention engine, with trajectory, domains, desk membership, and an explicit evidenceStatus (receipted rows carry source-attributed evidence URLs; unreceipted rows are labeled leads). Measured property: Measured association: flagged markets repriced materially at 1.62x the rate of matched unflagged controls (95% CI 1.40-1.81, shock-days excluded, controls reweighted to the flagged cohort; n=1848 flagged vs 79307 controls, window 2026-03-29 to 2026-08-31, as of 2026-09-01). An attention-leads-movement association — never a directional claim.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many signals (default all flagged)
directionNoFilter by trajectory: rising|fading|steady|new

TDQS

A4.2/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it discloses a rate limit (500/day), no model work, payload parity with GET /api/signals, evidenceStatus semantics, and the caveat that the association is not a directional claim. This is substantive behavioral context, not a repetition of schema.

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

Conciseness3/5

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

The opening is well front-loaded, but the 'Measured property: Measured association' block is verbose, repeating 'Measured' and including detailed CI and sample-count stats. Those numbers add context but are more than needed for tool selection or invocation.

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?

Despite having no output schema, the description explains what a caller will receive (signal rows with trajectory, domains, desk membership, evidenceStatus, and source URLs for receipted rows). Combined with rate/cost info and the non-causality warning, this is complete enough to invoke the tool correctly; the main omission is explicit routing to alternative sibling tools, which is not essential here.

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

Parameters3/5

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

Both parameters are already fully described in the schema (limit default and direction filter values), so the description has little to add. It connects trajectory to the domain but does not need to; baseline 3 is appropriate.

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 names a concrete resource ('the full machine-readable signal feed') and specifies its contents: every entity flagged by the attention engine along with trajectory, domains, desk membership, and evidenceStatus. Phrases like 'START HERE for browsing' and 'cheap read' distinguish it from more targeted siblings such as get_entity or top_accelerating.

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?

Explicitly tells the agent this is the starting point for browsing and frames it as the cheap read with a 500/day quota and no model work. It does not enumerate when-not-to-use cases or name alternatives, so it falls short of a 5.

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.

Resources