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Shodan

by io.github.pipeworx-io

Validate Claim

validate_claim
Read-onlyIdempotent

"Is it true that…" / "fact check" / "verify the claim that…" / "did X really…" / "was Y actually…" / "confirm or refute" / "true or false" — natural-language claim verification against authoritative sources. Use whenever the agent needs to check whether something a user said is factually correct. Company-financial claims (revenue, net income, cash for public US companies) verify via the structured SEC EDGAR + XBRL fast path with exact percent-delta math; ANY OTHER factual claim (macro statistics, rates, prices, drug data, records) automatically falls through to the grounded pipeline — routed to the right live source, answered with verbatim evidence, then judged. Returns a verdict (confirmed / approximately_correct / refuted / inconclusive / unsupported / could_not_verify), the grounded or structured actual value with pipeworx:// citation, and reasoning. IMPORTANT for callers: could_not_verify means the check did not happen (our LLM or source failed) and carries verification_error{stage,detail} — it is NOT evidence for or against the claim, and must not be shown as one. unsupported means we looked and cover no source for it. Replaces 4–6 sequential calls (NL parsing → entity resolution → data lookup → comparison).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimYesNatural-language factual claim, e.g., "Apple's FY2024 revenue was $400 billion" or "Microsoft made about $100B in profit last year".
tolerance_pctNoMax percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. Changed1 schema field changed
    • addedInput schema / properties / tolerance_pct
      Added value: +{
+  "description": "Max percent deviation still graded approximately_correct (0.5–50). Overrides the tolerance implied by the claim wording — set 1–2 for hallucination detection where any material error must be refuted. Default: implied by wording, capped at 5.",
+  "type": "number"
+}
  2. First observed

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations (readOnlyHint, openWorldHint, idempotentHint, destructiveHint), the description discloses critical behavioral nuances: the verdict vocabulary, the semantic difference between could_not_verify (check did not happen, carries verification_error, not evidence) and unsupported (no source covers it), and the two routing paths (structured SEC EDGAR vs. grounded pipeline). This adds substantial value and does not contradict any annotation.

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 long but front-loaded with a purpose statement and trigger phrases, then logically moves through usage, return values, and critical caveats. Every sentence conveys important information, especially the could_not_verify warning and the 'replaces 4–6 calls' efficiency note. The opening list of phrases is slightly repetitive, but overall the structure earns its length for the tool's complexity.

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?

With no output schema, the description carries the burden of explaining return values and behavior. It does so thoroughly: enumerates all verdicts, describes the actual value with pipeworx:// citation, explains could_not_verify and unsupported, and outlines the two processing paths. This provides enough context for an agent to use the tool correctly and interpret results, even without an output schema.

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% and the schema already thoroughly explains both parameters, including tolerance_pct's override behavior and hallucination-detection usage. The description adds only a minor tie-in via 'exact percent-delta math,' which reinforces but does not extend the parameter meaning. This meets the baseline for well-documented schemas.

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 natural-language trigger phrases and explicitly states the core function: 'natural-language claim verification against authoritative sources.' It distinguishes the tool by covering both SEC EDGAR/XBRL financial claims and a grounded pipeline for other claims, and clearly names it as a replacement for 4–6 sequential calls. This is a specific verb+resource that differentiates it from sibling tools like ask_pipeworx or deep_research.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives an explicit when-to-use: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates the financial vs. non-financial paths. It does not explicitly state when not to use it or name alternative tools, but the 'Replaces 4–6 sequential calls' line provides implicit guidance against doing the multi-step pipeline manually. This is clear context but lacks exclusionary directing.

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

A3.6/5.0
Disambiguation2/5

Several tool families have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx right now, ask_pipeworx_grounded and validate_claim both verify claims against sources, and polymarket_edges/polymarket_arbitrage/bet_research all surface prediction-market opportunities. Agents would need to read very long descriptions to distinguish overlapping intents, and would likely misroute requests.

Naming Consistency3/5

All names use snake_case, which is a consistent base, and subgroups (shodan_host*, ask_pipeworx*, polymarket_*) follow internal patterns. However, conventions mix verb-first (ask_pipeworx, validate_claim, resolve_entity) with noun-first (entity_profile, bet_research, recent_alerts), and parallel functionality is named with inverted ordering (ai_visibility_check vs scan_competitor_ai_presence).

Tool Count2/5

34 tools is above the comfortable range for a single server and the breadth feels bloated, especially with five highly niche Polymarket tools. More critically, the server is named 'Shodan' but only 3 of 34 tools are Shodan-related, so the count is badly mismatched with the stated identity.

Completeness2/5

For a server claiming to be Shodan, the surface is severely incomplete — only host lookup, search, and count are present, with no DNS, alerting, or other standard Shodan operations. For the actual Pipeworx/Polymarket domain implied by most tools, coverage is fuller but still has gaps such as no direct tool to fetch a pipeworx:// citation URI, and the memory/subscription features feel bolted on rather than integral.

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