signal-bureau

get_calibration

Deprecated: this tool is winding down — for evaluating or reading the maintained record, use get_truth_object, get_entity/search_entities, and todays_brief instead. Still served here: the grading dataset for the market sensor we cite, and only that — the prediction-market price (crowd) and the naive null it is scored against (base_rate), against how reality resolved (Brier score, log loss, skill vs base rate, 95% CIs), each price captured while its market was OPEN and graded as it matured, so the grade cannot be back-filled. No forecaster of ours is published, here or in the full dataset: we sell a maintained, source-traced record and cite market prices as one graded sensor, not a forecast of ours. Full dataset with reliability bins: GET /api/calibration-data.

Input Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

TDQS

A3.9/5.0

Behavior3/5

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

With no annotations, the description carries the full burden. It adds significant context: deprecated status, what data is served (grading dataset for market sensor), that it is scored while open and graded as matured (cannot be back-filled), and that no forecaster is published. However, it does not explain behavior like response format or potential errors, but for a deprecated tool, the disclosure is fairly thorough.

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 description is a long single block of text, somewhat dense and not front-loaded. It is informative but could be broken into clearer sections (e.g., deprecation notice, what it returns, what it does not return). It earns its place but could be more structured.

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 tool is simple (no parameters, no output schema), the description covers the key aspects: deprecation, alternatives, what data is included, and what is not included. It also points to a full dataset endpoint. This is complete enough for the agent to decide if and how to use it.

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 0 parameters, and schema coverage is 100% (trivially, since there are no parameters). The description adds context about what data is returned (fields like crowd, base_rate, Brier score, log loss, etc.), which is useful for the agent to understand the output even without an output schema.

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

Purpose4/5

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

The description states it is deprecated and what it serves: the grading dataset for the market sensor (crowd and base_rate) with performance metrics. It clearly identifies the resource and distinguishes it from siblings by explicitly naming alternatives (get_truth_object, get_entity/search_entities, todays_brief).

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?

Explicitly states the tool is deprecated and tells users to use specific alternatives for maintained data. It also clarifies what is not included (no forecaster published) and directs to a full dataset endpoint. This provides clear when-to-use and when-not-to-use guidance.

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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