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Glama

Vessel Tracking

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. First observed

TDQS

A4.6/5.0
Behavior5/5

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

Despite readOnlyHint and idempotentHint annotations, the description adds substantial behavioral context: the dual-path routing (SEC EDGAR vs grounded pipeline), the full list of verdicts, pipeworx:// citations, and critical clarification that could_not_verify is a failed check, not evidence. This goes well beyond what annotations convey.

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 lengthy but well-structured: trigger phrases, purpose, routing, return values, warnings, and performance note. Each sentence contributes unique value, but it could be tightened without losing information. Overall, it's efficiently organized for a complex tool.

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?

Since there is no output schema, the description must explain return values, and it does: verdict types, actual value with citation, and reasoning. It also covers error semantics (could_not_verify vs unsupported), the two routing modes, and the tool's benefit over multi-call pipelines. No major gap remains.

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?

The schema already describes both parameters with 100% coverage, but the description enriches them further. Specifically, it explains tolerance_pct as an override of the wording-derived default, capped at 5, and recommends 1–2 for hallucination detection. This adds actionable semantics 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 specific verb+resource: natural-language claim verification against authoritative sources. It includes trigger phrases and distinguishes itself from sibling tools by focusing solely on fact-checking. Even without naming siblings, it's unambiguous what this tool does.

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 explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims and other factual claims, providing routing guidance. However, it doesn't mention alternative tools to use instead or when not to use this tool, so it doesn't earn a 5.

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

A3.7/5.0
Disambiguation2/5

Several tools have significantly overlapping purposes, especially the ask_pipeworx family, deep_research, and validate_claim, plus a dense cluster of polymarket_* tools and two AI-visibility checkers. The ship-related tools are distinct, but an agent would struggle to choose among the many broadly similar query/research tools.

Naming Consistency3/5

Names are mostly snake_case and readable, but they follow no consistent convention: generic one-word verbs like remember and forget sit alongside branded names like ask_pipeworx, noun-style names like entity_profile, and prefix families like polymarket_*. The inconsistency is noticeable but not chaotic.

Tool Count2/5

34 tools is well above the well-scoped range, and the vast majority are unrelated to the server name 'Vessel Tracking'. The live-ship tools are a tiny minority buried inside a broad general-purpose data, research, and prediction-market platform, making the overall set feel bloated and misaligned with its stated identity.

Completeness1/5

As a vessel-tracking server, the surface is severely incomplete: only ais_coverage_check, live_ship_position, and live_ships_in_area relate to shipping, with no vessel lookup by name/IMO, no historical positions, no voyage data, and no port-call information. The actual tool set is rich as a general data platform, but that is not what the server name promises.

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