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Glama

Useragent

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

A4.9/5.0
Behavior5/5

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

Annotations indicate readOnlyHint, idempotentHint, and non-destructive nature. Description adds valuable behavioral details: returns top-N relevant tools with full schemas and examples, and each result is 'ready to call directly, no second schema lookup needed.' 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?

Description is concise (4 sentences) and front-loaded with purpose. Each sentence earns its place: purpose, usage examples, key behavior (returning ready-to-call results), and explicit call-to-action ('Call this FIRST'). No fluff.

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 adequately explains that the tool returns a list of tools with names, descriptions, and schemas. For a discovery tool, this is complete. The context of being a first step among many sibling tools is well addressed.

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% (baseline 3). Description adds meaning by explaining the 'query' parameter expects natural language, listing aliases, and providing examples. The limit parameter is also described. This goes beyond the schema's descriptions.

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 the tool's purpose: 'Find tools by describing the data or task.' It lists many specific data domains (SEC filings, FDA drugs, etc.) and explicitly distinguishes itself as the discovery tool among siblings like deep_research and entity_profile.

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 advises 'Call this FIRST when you have many tools available and want to see the option set (not just one answer).' This provides clear when-to-use guidance and implies when not to use (when you already know the tool). No need for alternatives since the sibling list is provided.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Useragent toolsInspect
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TDQS

A3.9/5.0
Disambiguation2/5

ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all perform overlapping routed-search functions; ask_pipeworx_beta is even documented as currently identical to ask_pipeworx. The three Polymarket discovery tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk) also have blurry boundaries around finding vs. validating vs. executing on edges.

Naming Consistency4/5

Nearly all tools use snake_case with a verb_noun or descriptive pattern (ask_pipeworx, validate_claim, resolve_entity, list_subscriptions). Minor deviations exist — bare verbs like remember/recall/forget and noun_first names like bet_research or entity_profile — but the convention is largely predictable and readable.

Tool Count2/5

32 tools is heavy, and the set spans unrelated domains: Pipeworx data routing, prediction-market trading, agent memory, subscription management, npm dependency checks, user-agent parsing, and llms.txt generation. The sub-clusters each earn their place individually, but as a single server surface the count is unjustifiably large and scattershot.

Completeness3/5

The Pipeworx research surface is quite complete (lookup, grounded answers, deep research, entity profiles, comparisons, validation, entity resolution, discovery, feedback), and memory/subscription lifecycles are fully covered. However, the server has no coherent single domain — user-agent parsing (the server's namesake) has only one tool, while unrelated utilities like generate_llms_txt and scan_dependency appear with no supporting ecosystem.

Resources