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Glama

Data Norfolk

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

A4.7/5.0
Behavior4/5

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

Annotations already provide readOnlyHint, idempotentHint, destructiveHint. The description adds behavioral context about returning top-N relevant tools with full schemas ready to call, which goes beyond annotations. No contradiction with 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 paragraph, front-loaded with purpose, every sentence adds value: purpose, usage examples, output description, strategic guidance. No wasted words.

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 no output schema, the description explains the return format comprehensively: top-N tools with names, descriptions, full input schemas. It also covers parameter semantics through examples and aliases. Complete for a discovery tool.

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 coverage is 100%, so baseline is 3. The description adds meaning by providing natural language examples for the query parameter and explaining that multiple aliases are accepted, which enhances understanding 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 it finds tools by describing the data or task. It uses specific verb 'find' and resource 'tools', and distinguishes from siblings by advising to call it first when needing to browse the tool set.

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 'Use when you need to browse, search, look up, or discover what tools exist for' with specific examples. Also advises to call it FIRST when many tools are available, providing clear when-to-use and when-not-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

A3.6/5.0
Disambiguation1/5

ask_pipeworx and ask_pipeworx_beta are currently identical, deep_research overlaps ask_pipeworx for multi-faceted questions, and the five Polymarket tools plus bet_research all target the same market-analysis space. Several tools appear to do the same thing, and even detailed descriptions can't fully separate them.

Naming Consistency3/5

All names are lowercase snake_case and mostly readable, but the set mixes bare nouns (datasets, query, recall) with verb phrases (generate_llms_txt, validate_claim) and domain-prefixed compounds (polymarket_edges, pipeworx_trending). The ask_pipeworx family is internally consistent, but the overall pattern is not uniform.

Tool Count2/5

34 tools is well above the 25-tool threshold for a coherent set, especially since many are meta-tools (suggest_questions, discover_tools, pipeworx_feedback, pipeworx_trending) or one-off utilities (generate_llms_txt, scan_dependency). The server tries to cover data lookup, prediction markets, memory, subscriptions, and AI visibility in a single surface.

Completeness3/5

Within each subdomain (data lookup, memory, subscriptions, Polymarket) the main workflows are covered, and the memory/subscription clusters have full CRUD. But the server name promises Norfolk Open Data, which is barely represented by three read-only tools, and odd one-offs like generate_llms_txt and scan_dependency have no supporting ecosystem.

Resources