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luxalgo-mcp-server

by io.github.LuxAlgo
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List Market Trackers datasets

trackers_datasets
Read-only

The Market Trackers catalog: every dataset of US public-record market data the LuxAlgo pipeline publishes as CC0 dumps — congressional trades, insider (Forms 3/4/5) transactions, 13F holdings, federal contracts and grants, lobbying filings, FINRA short-sale volume, granted patents, clinical trials, FDA drug events, CFTC positioning, federal bills, FEC campaign finance, hearing transcripts, Federal Reserve communications, committee assignments, Wikipedia pageviews. Returns each dataset's row count, freshness, the years with data (live tree vs deep-history archives), and whether it is ticker-searchable. Pass dataset for the full field roster, filterable paths, caveats, per-year coverage, source health, and dump URLs — read it before composing trackers_query filters.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
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."
datasetNoOne dataset for the detailed view; omit to list all

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 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.\""
  2. Added

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and openWorldHint=true, so the safety profile is covered. The description adds behavioral detail about the output (row count, freshness, live-tree vs deep-history coverage, ticker-searchability) and the detailed-view contents, which goes beyond annotations and helps set expectations.

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 three sentences that move from catalog scope to return values to usage guidance, with no filler. The first sentence is long, but every item in the list earns its place by naming a dataset category; the actionable advice is saved for the end.

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 metadata/list tool, the description covers the dataset set, summary fields, the detailed view, and its relationship to trackers_query. With annotations supplying the read-only/open-world profile and no output schema, the description is sufficiently complete for correct invocation; minor details like pagination are not essential here.

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?

With 100% schema description coverage, the baseline is 3, but the description adds meaning beyond the schema by explaining what the dataset parameter provides (full field roster, filterable paths, caveats, per-year coverage, source health, dump URLs). This helps agents decide whether to pass a dataset or omit it.

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 names the resource ('Market Trackers catalog') and the action ('Lists'), and enumerates the dataset types and returned metadata (row count, freshness, years, ticker-searchability). It also references trackers_query, letting an agent distinguish this discovery tool from the query tool without opening the schema.

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?

It explicitly states when to pass the optional dataset ('Pass dataset for the full field roster...') and instructs agents to 'read it before composing trackers_query filters', giving a concrete precondition for using a sibling tool. This provides clear routing without ambiguity.

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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