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autario-mcp

by io.github.Autario
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list_politicians

Read-onlyIdempotent

List the members of the US House of Representatives who filed Periodic Transaction Reports (STOCK Act stock trade disclosures published by the Clerk of the US House), with their number of reported trades, purchases, sales, most traded asset and latest report date. Use it to find the slug get_politician_trades takes (first-last-state, e.g. josh-gottheimer-nj). Senate trades are not included.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "list_politicians"
  }
}

Input Schema

NameRequiredDescriptionDefault
formatNoOutput wire format for this MCP call. Default 'toon' (Token-Oriented Notation, fewest tokens, best for tabular rows). 'compact' = minified JSON. 'json' = pretty JSON for readability. The REST API always returns JSON regardless.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.5/5.0
Behavior4/5

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

Annotations already establish readOnly, idempotent, non-destructive, closed-world behavior, so the bar is lower; the description adds useful context beyond them, disclosing the data provenance (Clerk of the US House), the regulatory basis (STOCK Act), and the returned summary fields. It does not mention result size, pagination, or ordering, which are the remaining behavioral gaps for a list endpoint.

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?

Content is front-loaded: what is listed, what fields come back, then the routing advice to the sibling tool, then the scope exclusion. Despite a long opening clause, every sentence carries decision-relevant information with no filler.

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?

With no output schema, the description correctly compensates by enumerating the returned summary fields (trade counts, purchases, sales, most traded asset, latest report date) and the slug format needed to continue. Nothing an agent needs to call this tool and chain it into get_politician_trades is missing.

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?

There is only one parameter (format) and the schema describes it at 100% coverage, including the enum meanings, so the baseline is 3. The description adds nothing about output format selection or how the wire format interacts with the tabular summary, so it does not exceed 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 names a specific verb and resource ('List the members of the US House of Representatives who filed Periodic Transaction Reports') and defines scope precisely via the STOCK Act / Clerk of the House source. It also explicitly excludes Senate trades, so an agent can distinguish it from broader congress-trade tools like congress_trades without inspecting schemas.

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 states an explicit downstream purpose: 'Use it to find the slug get_politician_trades takes (first-last-state, e.g. josh-gottheimer-nj)', which names the alternative tool and the condition for chaining into it. It also gives a clear when-not boundary ('Senate trades are not included'), leaving little to inference.

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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