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Glama

H1b

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.

TDQS

A4.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false. Description adds that results are ready to call directly with no second schema lookup, enhancing transparency beyond annotations.

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?

Description is comprehensive but not overly long; it front-loads the purpose and uses clear structure. A minor reduction in domain listing could improve conciseness.

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?

Given no output schema, description explains that returns top-N tools with names, descriptions, schemas, examples, ready to call. Also covers usage context and aliases, making it complete.

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% with parameter descriptions. Description adds context on query aliases and limit bounds, and provides example queries (FDA drug approvals, housing market).

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?

Description clearly states 'Find tools by describing data or task' with specific verb+resource. It lists many domains (SEC filings, financials, etc.) and distinguishes itself from sibling tools as the discovery tool.

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 describes when to use it ('browse, search, look up, discover').

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

A4.1/5.0
Disambiguation3/5

Several overlapping clusters exist: ask_pipeworx/ask_pipeworx_beta/ask_pipeworx_grounded/deep_research all handle research questions, polymarket_edges and polymarket_arbitrage both scan for trading opportunities, and discover_tools/suggest_questions serve similar discovery purposes. The long, use-case-specific descriptions help, but an agent could still easily pick the wrong tool among these near-duplicates.

Naming Consistency4/5

Most tools follow a snake_case verb_noun or noun pattern (resolve_entity, validate_claim, list_subscriptions, h1b_salary), which is fairly consistent. However, there are deviations: bare verbs like recall/remember/forget/subscribe, noun-only phrases like entity_profile and recent_changes, and the ask_pipeworx_beta suffix variant break the pattern slightly.

Tool Count2/5

34 tools is well past the 25+ threshold, and the server is named 'H1b' while only 3 of the 34 tools relate to H-1B data. The rest is a sprawling mix of data research, prediction-market analytics, memory, subscriptions, AI visibility checks, and unrelated utilities like generate_llms_txt and scan_dependency — a severe scope mismatch.

Completeness4/5

As a de facto Pipeworx research platform, the surface is nearly complete: open-ended queries, grounded evidence mode, deep multi-source research, entity resolution, comparison, claim validation, subscriptions, memory, and feedback. The H-1B sub-domain covers employer, salary, and top-sponsor lookups, though the mention of green cards is a small mismatch since only LCA data is provided.

Resources