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Stratz

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

A4.7/5.0
Behavior5/5

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint. The description adds behavioral traits: returns top-N relevant tools with full input schemas and curated examples, ready to call directly without second lookup. No contradictions.

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 front-loaded with key purpose and usage, but includes a lengthy list of example domains which adds verbosity. However, it is well-structured and each sentence serves a purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (6 params, no output schema, many siblings), the description covers purpose, usage, output format (top-N with schemas), and example queries. It does not mention rate limits or empty results, but is otherwise complete enough for an AI agent.

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 description does not need to add parameter details, but it provides contextual examples (e.g., 'analyze housing market trends') that illustrate usage. The description also notes the alias pattern implicitly through examples, adding value 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's purpose with a specific verb ('Find') and resource ('tools by describing the data or task'). It distinguishes itself from siblings by being the meta-tool for discovering other tools, listing numerous example domains (SEC filings, FDA drugs, etc.).

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 to use when browsing or discovering tools, and to call this FIRST when many tools are available. It also implies not to use when you already know the specific tool. Provides clear context for when this tool is appropriate.

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

B3.3/5.0
Disambiguation2/5

Several tools have overlapping or intentionally duplicated purposes: ask_pipeworx/ask_pipeworx_beta currently behave identically, and the Polymarket cluster (bet_research, polymarket_arbitrage, polymarket_edges, polymarket_fill_risk, polymarket_kalshi_spread) presents multiple scanners with fuzzy boundaries. The long descriptions help, but the set as a whole is hard to navigate without close reading.

Naming Consistency2/5

Naming is a mix of bare single nouns (hero, match, meta, remember, forget), snake_case verb-first names (ask_pipeworx, compare_entities, generate_llms_txt), and noun-first compounds (pipeworx_feedback, bet_research, scan_competitor_ai_presence). There is no consistent verb_noun or noun-verb convention across the set.

Tool Count2/5

At 43 tools, the server bundles at least five unrelated domains (Dota 2 stats, Pipeworx data querying, Polymarket analytics, memory, subscriptions, AI visibility). That is far too many for a focused MCP server, and the mix makes the surface feel like a grab bag rather than a purpose-built toolkit.

Completeness4/5

Each subdomain individually has solid coverage: Dota 2 has heroes/matches/players/tournaments/meta plus a GraphQL fallback, the data layer has discovery + routing + grounding + validation, and memory/subscriptions have full lifecycle operations. The only real gap is cohesion across domains; within each slice there are no obvious dead ends.

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