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mcp-server

by io.github.tickerbot

tickerbot_scan

Run a SQL WHERE clause against the live ticker universe (or against a past moment with asof — unlimited depth on every plan). Returns matching tickers sorted by chosen column, OR — with group_by — aggregate rollups instead of rows (breadth stats: "count of tickers above their 200dma by sector", "median RSI by sector on 2026-03-03"). The q grammar is a flat WHERE: column names from the schema, AND/OR/NOT, comparison operators, numeric/string literals. No JOIN or subqueries. Example: gap_up AND market_cap < 2000000000 AND NOT earnings_this_week.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qYesSQL WHERE expression. Max 4000 chars.
dirNoSort direction.
asofNoOptional YYYY-MM-DD or ISO timestamp for a historical scan. Unlimited depth on every plan.
fullNoRow mode: return the FULL wide row for each match (every column) instead of the slim default projection.
limitNoPage size. Max 100. Default 50. Aggregate mode does not paginate — response sets `truncated: true` when groups were cut.
orderNoSort column. Default: the 1-day change column (rows) / tickers (aggregate).
cursorNoOpaque cursor (row mode only).
fieldsNoComma-separated extra columns to include (row mode only).
havingNoAggregate filter (requires group_by). Example: `COUNT(*) >= 10`.
selectNoAggregate output items (requires group_by). Default: group keys + COUNT(*) AS tickers. Aggregates: count/avg/sum/min/max/stddev/string_agg + FILTER (WHERE …). Alias items with AS. Example: `sector, COUNT(*) AS n, AVG(rsi_14) AS avg_rsi`.
group_byNoAGGREGATE MODE: 1–6 comma-separated group keys (columns or expressions, e.g. `sector`). Results become rollup rows instead of tickers. Alias a key with `AS` to name its JSON key; un-named expressions are named for you.
universeNoOptional universe slug.

TDQS

A4.2/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and it delivers: it discloses dual output modes, the asof historical-scan capability with unlimited depth, grammar limitations (flat WHERE, no JOIN/subqueries), and sorting behavior. It does not explicitly confirm read-only semantics or discuss rate limits, but the scan framing and sibling set make the read nature clear.

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?

Four sentences with zero filler: core capability, output modes with illustrative examples, grammar constraints, and a concrete worked query. The most important scoping information is front-loaded, and every sentence earns its place.

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 complex 12-parameter, dual-mode tool, the description covers the essential decision points (mode selection, grammar rules, historical scanning) and the schema documents every parameter. The only gap is the absence of output-shape guidance, which is partially mitigated by the aggregate-mode examples and the truncated:true note in the schema.

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 3, but the description adds real value beyond the schema: it defines the q grammar (AND/OR/NOT, comparators, literals, flat WHERE), clarifies that group_by switches the result shape, and gives concrete valid expressions. This meaningfully compensates for the schema's terse 'SQL WHERE expression' on q.

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+resource ('Run a SQL WHERE clause against the live ticker universe') and explains both operating modes (row results vs aggregate rollups). The worked example and breadth-stat examples make the tool's purpose unmistakable, and the scan capability is clearly distinct from the list/get/subscribe siblings.

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

Usage Guidelines3/5

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

The use case is strongly implied — filter the universe with SQL-like predicates, run historical scans via asof, or compute grouped breadth stats. However, no sibling is named and there is no explicit when-to-use/when-not-to-use guidance versus alternatives like tickerbot_list_tickers, tickerbot_get_ticker, or tickerbot_subscribe_scan.

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

A3.9/5.0
Disambiguation4/5

Most tools target distinct resource-action pairs, but get_ticker and get_ticker_history overlap (both return a single historical row via asof), and the four subscribe_* tools are thin wrappers around create_webhook, creating some ambiguity. Detailed descriptions mitigate but don't eliminate confusion.

Naming Consistency4/5

The tickerbot_ prefix and verb_noun pattern are consistent (create_*, get_*, list_*, delete_*, update_*), but subscribe_* deviates from create_* for webhooks, and scan/search_news are bare verbs. Overall predictable.

Tool Count2/5

35 tools is excessive for the domain; many could be consolidated (e.g., four subscribe_* variants into a single parameterized webhook creator, get_ticker/get_ticker_history into one). The breadth of data types justifies some volume, but this exceeds the 25-tool threshold for coherence.

Completeness5/5

Full lifecycle coverage for universes, custom signals, and webhooks; extensive read-side for tickers, series, bars, events, news, and scans. No critical dead ends: anything creatable can be updated/deleted/tested, and data lookups have appropriate query tools.

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