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Read-onlyIdempotent

Web search returning the top results as markdown, via Jina. Needs a Jina API key (pass via _apiKey) — Jina stopped serving anonymous requests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesThe search query, e.g. "best coffee in Seattle".
_apiKeyNoYour Jina API key. Required — Jina no longer serves anonymous requests. Pipeworx injects its own when one is configured; free keys at https://jina.ai/reader.

TDQS

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds beyond annotations by disclosing the external dependency on Jina, the API key requirement, and the markdown return format. It also mentions that Pipeworx injects its own key when configured, which sets expectations about key handling. 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?

One tightly packed sentence in the description plus detailed schema parameter descriptions. Every element earns its place: the provider, the return format, the key requirement, and why it's needed. 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?

For a simple two-parameter read-only search tool with 100% schema coverage, this is complete. The description covers the key prerequisite (API key), the service, the output format, and the fallback behavior (Pipeworx injects its key). No output schema exists but none is essential here; the markdown format hint suffices.

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 description coverage is 100%, so schema already documents both parameters well. The description adds meaning by explaining the markdown return format and the 'via Jina' context, which helps an agent understand why _apiKey is required. It doesn't add syntax details beyond the schema, but baseline 3 is exceeded slightly by the external-service context.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states a specific verb+resource: web search returning top results as markdown via Jina. It clearly distinguishes from siblings like search_within and read_url, though it doesn't explicitly name alternatives. The phrase 'web search' plus 'returning... as markdown' gives enough clarity for an agent to know what it does.

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?

The description explicitly states when to use this tool (web search) and the critical prerequisite (Jina API key required, passed via _apiKey). It also discloses that Jina stopped serving anonymous requests, so an agent knows not to attempt without a key. This is strong guidance for usage.

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

A4/5.0
Disambiguation2/5

Multiple tools have overlapping purposes, such as ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and specialized tools like entity_profile or validate_claim that can answer similar questions. This creates ambiguity for an agent trying to select the correct tool.

Naming Consistency5/5

All tool names follow a consistent snake_case pattern, with most using a verb_noun structure (e.g., ask_pipeworx, compare_entities, resolve_entity). There are no mixed conventions or chaotic naming.

Tool Count3/5

With 31 tools, the server is on the heavy side. While each tool has a distinct purpose, the number is borderline for a coherent set and could be streamlined, especially given the overlapping functionality.

Completeness3/5

The tool set covers a wide range of query and analysis tasks, including data lookup, comparison, betting research, memory, and subscriptions. However, there are notable gaps (e.g., no update/delete for most data, no user management) and some tools seem out of place (e.g., generate_llms_txt).