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AIsa Finance

Search financial statements

post_financial_financials_search_screener
Destructive

Find tickers that match numeric conditions. Body takes filters — each a field, an operator and a value — plus limit. Returns results with ticker, currency, sector, industry and whichever filtered field was matched. This is the only tool here that works without knowing a ticker in advance; everything else takes one as input. Filterable fields are the metric names get_financial_financial_metrics returns. Use it to build a candidate list, then pull detail on each name with the statement or metric tools.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoThe maximum number of results to return.
periodNoThe time period for the financial data.ttm
filtersYesAn array of filter objects to apply to the search.
currencyNoThe currency of the financial data.
order_byNoThe field to order the results by. Use -field to order in descending order.ticker
historicalNoWhether to return historical financial data.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior1/5

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

The description presents the tool as a search/filter operation that 'finds' tickers and 'returns results', implying no side effects. However, the annotations declare destructiveHint=true and readOnlyHint=false, creating a direct contradiction. No effort is made in the description to reconcile this or disclose destructive behavior.

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 compact and front-loaded: it states the core purpose, mentions the request shape, describes the result shape, and gives workflow guidance in a few sentences. There is only minor redundancy, such as re-emphasizing the ticker choice, but overall it is efficient.

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 that there is an output schema, 100% schema coverage, and 3 enumerated parameters, the description is sufficiently complete for the main search workflow. It covers the candidate-list use case and output content, so the agent is unlikely to misunderstand how to invoke the tool; the main unresolved issue is the annotation contradiction.

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?

Schema coverage is 100%, so the baseline is 3. The description does add some value by explaining the filter structure (field/operator/value) and pointing to get_financial_financial_metrics as the source of filterable fields, but it does not deeply extend the schema-provided parameter meaning.

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's purpose: find tickers matching numeric conditions. It also distinguishes it from siblings by noting it is the only tool here that works without knowing a ticker in advance, which is actionable differentiator.

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?

The description explicitly says when to use it: to build a candidate list, then pull details on each name with statement or metric tools. It also tells the agent that all other tools require a ticker, so the intended workflow is clear.

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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