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luxalgo-mcp-server

by io.github.LuxAlgo
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Query a Market Trackers dataset

trackers_query
Read-only

Search one Market Trackers dataset by ticker, free text, exact field values, and event-date range, with paging and newest/oldest ordering. Data is read from year-sharded CC0 dumps: pass years (or since/until) to choose which years to read — default is the newest year with data. Deep-history years (see archiveYears in trackers_datasets) can be tens of MB compressed each, so read them one or two at a time; the tool refuses selections over its byte budget and says how to narrow. Every row carries provenance.sourceUrl (the SEC filing, disclosure, award, or record it came from). Examples: insider purchases at NVDA in 2024 → dataset insider-transactions, ticker NVDA, years [2024], where {code: 'P'}; a senator's trades → congress-trades, text 'Tuberville'; who lobbied on a bill → lobbying-filings, text 'H.R.1234'.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNoOrder by event date (default newest)
textNoCase-insensitive substring over the dataset's name/title fields (member, insider, issuer, recipient, registrant and client, sponsor, assignee, bill title, …); see textPaths in trackers_datasets
limitNoRows to return (default 25, max 100)
sinceNoEarliest event date (YYYY-MM-DD, YYYY-MM or YYYY), inclusive
untilNoLatest event date, inclusive
whereNoExact field matches by dot path, e.g. {"side":"buy"}, {"member.state":"CA"}, {"code":"P"}, {"formType":"4"}; string comparisons are case-insensitive, arrays match when any element does
yearsNoEvent years to read (max 8); default is the newest year with data. Prefer one year at a time for deep-history datasets.
offsetNoRows to skip, for paging (default 0)
tickerNoTrading symbol, case-insensitive (e.g. 'NVDA'); matches the dataset's ticker field(s). Only datasets flagged tickerSearchable carry tickers.
contextYesExplain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as "a user", "the customer", or "an account". Example: "Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution."
datasetYesDataset id, from trackers_datasets

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed7 schema fields changed
    • changedInput schema / $schema
      Previous value: -"http://json-schema.org/draft-07/schema#"New value: +"https://json-schema.org/draft/2020-12/schema"
    • changedInput schema / properties / context / description
      Previous value: -"Explain why you are calling this tool and how it fits into the user's overall goal. This parameter is used for analytics and user intent tracking. YOU MUST provide 15-25 words (count carefully). NEVER use first person ('I', 'we', 'you') - maintain third-person perspective. NEVER include sensitive information such as credentials, passwords, or personal data. Example (20 words): \"Searching across the organization's repositories to find all open issues related to performance complaints and latency issues for team prioritization.\""New value: +"Explain in 15-25 words, in third person, why this tool is called and how it supports the user's goal. For analytics only. You MUST describe only the abstract purpose of the tool call. NEVER include, repeat, paraphrase, or infer personal, sensitive, or identifying information from the user request or tool results, including names, emails, phone numbers, IPs, IDs, or credentials. You MUST generalize specific entities into roles such as \"a user\", \"the customer\", or \"an account\". Example: \"Retrieving a customer's recent orders to investigate a billing issue and help support determine the appropriate resolution.\""
    • addedInput schema / properties / offset / maximum
      Added value: +9007199254740991
    • removedInput schema / properties / until / $ref
      Removed value: -"#/properties/since"
    • addedInput schema / properties / until / pattern
      Added value: +"^\\d{4}(-\\d{2}(-\\d{2})?)?$"
    • addedInput schema / properties / until / type
      Added value: +"string"
    • addedInput schema / properties / where / propertyNames
      Added value: +{
+  "type": "string"
+}
  2. Added

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true. The description adds substantial behavioral context beyond annotations: year-sharded CC0 dumps, the default to the newest year, the byte-budget refusal mechanism, and the provenance field (sourceUrl). It also warns about the cost of deep-history years. This is rich behavioral disclosure that goes far beyond the annotations.

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?

The description is a single well-organized paragraph that front-loads the core purpose, then adds operational details (year sharding, byte budget, provenance), and concludes with three concise examples. Every sentence contributes value, and the structure makes it easy to scan.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite 11 parameters, nested objects, and no output schema, the description covers essential call context: data source, filtering options, paging/ordering, byte-budget constraints, and provenance. It includes enough examples to guide an agent in common use cases. Nothing critical is missing for an agent to decide when and how to invoke 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?

Schema description coverage is 100% for all 11 parameters, so the baseline is 3. The description adds meaningful semantic context: it explains how `years` and `since`/`until` interact with the sharded dumps, advises on deep-history years, and illustrates `where` with concrete examples (e.g., {code: 'P'}). It also clarifies that `ticker` only applies to tickerSearchable datasets. This elevates it above baseline.

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 and resource: 'Search one Market Trackers dataset' and enumerates the search dimensions (ticker, free text, exact field values, event-date range, paging, ordering). It clearly differentiates from sibling tools like trackers_datasets (metadata), trackers_latest, and trackers_ticker by focusing on dataset-wide search.

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?

The description gives explicit usage guidance on year selection, including the byte-budget refusal and advice to read deep-history years one or two at a time. It provides three concrete examples (insider purchases, senator trades, lobbying filings) that map search patterns to datasets and filters. However, it does not explicitly contrast with sibling tools or state when NOT to use this tool, though the examples imply the appropriate scenarios.

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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