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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

A4.3/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is known. The description adds meaningful behavioral context: it returns top-N relevant tools with full schemas and curated examples, ready to call without a second schema lookup. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core purpose, then provides a useful list of example data domains, and ends with return behavior and a tactical 'call first' directive. It's slightly long but every sentence contributes information; the domain list earns its place by clarifying coverage.

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?

No output schema exists, so the description's explanation of return contents (top-N tools, names, descriptions, full input schemas with curated examples) is valuable. It covers what the tool does, when to use it, and what to expect from output, which is sufficient for a discovery tool of moderate complexity.

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

Parameters3/5

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

Schema coverage is 100%, with each parameter already described (including aliases for 'query'). The description mentions 'describing the data or task' which aligns with the query/task parameter, but adds no new syntax or format details beyond the schema. Baseline 3 applies.

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 opens with a specific verb and resource: 'Find tools by describing the data or task.' It clearly distinguishes this as a discovery tool from sibling tools, which are domain-specific, by stating it returns tool names, descriptions, and schemas.

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?

It provides explicit guidance: 'Use when you need to browse, search, look up, or discover what tools exist...' and 'Call this FIRST when you have many tools available and want to see the option set.' This clearly tells the agent when to invoke this tool and sets it apart as an initial exploration step.

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

C2.7/5.0
Disambiguation2/5

Several tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta (currently described as identical), ask_pipeworx_grounded, and deep_research all serve question-answering/research, and the six Polymarket tools heavily overlap in finding and evaluating trading edges. Individual descriptions are detailed, but an agent could easily route to the wrong variant.

Naming Consistency2/5

The set mixes conventions: Pipeworx tools mostly use verb_noun (ask_pipeworx, compare_entities, resolve_entity), but memory tools are bare verbs (remember, recall, forget), and the Ethereum tools are inconsistent (nft_metadata vs nfts_owned vs nft_owners, token_balances vs token_allowance). The lack of a uniform pattern makes the surface harder to predict.

Tool Count2/5

At 40 tools, the server is oversized for the apparent core purpose of an Alchemy Ethereum interface. There is also significant redundancy: multiple ask/deep-research entry points and a dense suite of Polymarket analysis tools add bulk that could be consolidated.

Completeness2/5

The Ethereum side is mostly read-only convenience wrappers (NFTs, tokens, asset transfers) plus a generic eth_call catch-all, but lacks dedicated transaction sending, block/transaction detail, logs, or ENS conveniences. The rest of the tool surface is a sprawling collection of unrelated data-research, memory, and subscription features, making the overall implied domain incoherent and likely to leave obvious gaps for users expecting a focused Ethereum server.