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Shodan

by io.github.pipeworx-io

Ask Pipeworx — Grounded

ask_pipeworx_grounded
Read-onlyIdempotent

Hallucination-resistant answer mode for high-stakes reads. Same routing as ask_pipeworx — picks the right tool from 5,743 across 1500 sources, fills arguments, fetches the data — then EXTRACTS the answer using ONLY what the tool result contains. Returns {answer, evidence (verbatim quote), confidence, source, fetched_at, refusal_reason:null} on success, OR an explicit refusal {answer:null, refusal_reason:"not_in_source"|"no_tool_match"|"tool_error"|"data_truncated"|"llm_error"} when the data doesn't directly answer. Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts (financial verdicts, legal claims, medical lookups, public statements). Costs one extra LLM call vs ask_pipeworx — prefer ask_pipeworx for casual lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoAlias for question.
textNoAlias for question.
inputNoAlias for question.
queryNoAlias for question.
promptNoAlias for question.
questionYesYour question in natural language. Accepts query, q, prompt, text, input as aliases.

TDQS

A4.7/5.0
Behavior5/5

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

The description goes well beyond the annotations by disclosing the refusal mechanism, the evidence format, the constraint that answers come only from tool results, and the extra LLM call cost. It also enumerates refusal_reason values. These are genuine behavioral traits not captured in readOnlyHint, openWorldHint, or idempotentHint.

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?

The description is information-dense and front-loaded with the core purpose, then flows through behavior, return shape, usage context, and tradeoff. Every sentence contributes decision-relevant detail, and no sentence is redundant filler.

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?

There is no output schema, so the description's inclusion of success and refusal return shapes is critical and well handled. It also addresses cost tradeoffs, use cases, and failure behavior, making the tool fully invocable and correctly selectable by an agent with no additional context.

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 description coverage is 100%, and all six parameters are documented aliases for the single `question` parameter. The description adds no direct parameter-specific semantics, but with full schema coverage the baseline of 3 is appropriate because the schema already carries the meaning.

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, differentiated purpose: 'Hallucination-resistant answer mode for high-stakes reads.' It clearly names the operation, the resource domain, and explicitly contrasts itself with ask_pipeworx, making it distinguishable from the main sibling without opening any schema.

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?

Usage guidance is explicit: 'Use whenever an answer will be quoted, cited, or acted on, and the agent must not invent facts' and 'prefer ask_pipeworx for casual lookups.' This gives both positive and negative selection criteria, exactly what an agent needs to route correctly.

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

A3.5/5.0
Disambiguation2/5

The server name 'Shodan' misleadingly implies a narrow focus on network security scanning, yet the tool set includes many unrelated tools for data lookup (ask_pipeworx, deep_research, etc.), memory management, and subscriptions. Within the pipeworx tools, there is overlap between ask_pipeworx, ask_pipeworx_grounded, and deep_research, which can confuse agents about which to choose.

Naming Consistency2/5

Tool names mix conventions: some use underscores (ai_visibility_check, shodan_host), others use lowercased phrases without clear partitioning (ask_pipeworx, deep_research, entity_profile). Verb-noun patterns are inconsistent (e.g., 'scan_competitor_ai_presence' vs 'compare_entities'). This lack of a predictable naming scheme increases cognitive load.

Tool Count2/5

With 33 tools covering distinct domains (Shodan scanning, pipeworx data, memory, subscriptions), the server feels overloaded and unfocused. While each tool may individually be useful, the bundling contradicts the principle of a single-purpose server. A more appropriate count for a focused Shodan server would be under 10 tools.

Completeness2/5

For the Shodan domain, only three tools are provided (host, host_count, host_search), missing key capabilities like DNS lookups, vulnerability search, or API key management. The pipeworx tool set is extensive but not the server's advertised purpose, leaving gaps in both areas. The addition of memory and subscription tools adds unrelated functionality without completing any single domain.

Resources