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Glama

Data Vermont

by io.github.pipeworx-io

Discover Tools

discover_tools
Read-onlyIdempotent

Find tools by describing the data or task. Use when you need to browse, search, look up, or discover what tools exist for: SEC filings, financials, revenue, profit, FDA drugs, adverse events, FRED economic data, Census demographics, BLS jobs/unemployment/inflation, ATTOM real estate, ClinicalTrials, USPTO patents, weather, news, crypto, stocks. Returns the top-N most relevant tools with names, descriptions, and full input schemas (with curated examples) — each result is ready to call directly, no second schema lookup needed. Call this FIRST when you have many tools available and want to see the option set (not just one answer).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for query.
taskNoAlias for query.
limitNoMaximum number of tools to return (default 20, max 50)
queryYesNatural language description of what you want to do (e.g., "analyze housing market trends", "look up FDA drug approvals", "find trade data between countries"). Accepts task, q, description, search as aliases.
searchNoAlias for query.
descriptionNoAlias for query.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A5/5.0
Behavior5/5

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

Annotations indicate read-only and idempotent, and description adds that it returns top-N relevant tools with full schemas and examples, ready to call directly. No contradiction and complete behavioral context.

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?

Description is concise, front-loaded with purpose, and every sentence adds value. No unnecessary verbosity.

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?

Even without an output schema, description fully explains what is returned (names, descriptions, schemas, examples), which is sufficient for a discovery tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but description adds value by explaining aliases (q, task, search, description) and providing natural language examples, making parameter usage clear beyond the schema.

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 finds tools by describing data or task, lists specific domains, and distinguishes it from sibling tools which are mostly specific data tools, not discovery.

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?

Explicitly says 'Call this FIRST when you have many tools available and want to see the option set' and 'use when you need to browse, search, look up, or discover', providing clear when-to-use guidance.

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

A4.3/5.0
Disambiguation4/5

Most tools have clearly distinct purposes, but the ask_pipeworx trio (ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded) and the polymarket cluster (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) could cause confusion without careful reading. However, the detailed descriptions effectively differentiate each tool's specific role.

Naming Consistency5/5

All tool names follow consistent snake_case convention with a verb-noun pattern (e.g., compare_entities, recent_changes, resolve_entity). There are no mixed conventions or chaotic naming, making the set predictable and easy to navigate.

Tool Count4/5

At 34 tools, the set is extensive but justified by the server's broad scope covering Vermont open data, Pipeworx data platform, prediction markets, and utility features. Each tool earns its place, though the count is slightly high compared to typical servers.

Completeness4/5

The tool surface covers data retrieval, entity analysis, prediction markets, memory, subscriptions, feedback, and claim validation. Minor gaps exist (e.g., no direct compliance tools), but the set is comprehensive for its stated purpose of data integration and analysis.

Resources