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Glama

Soilgrids

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

A4.5/5.0
Behavior5/5

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

Beyond the read-only, open-world, idempotent annotations, the description discloses critical behavioral nuance: the dual-path execution (SEC EDGAR/XBRL vs. grounded pipeline), the special meaning of 'could_not_verify' (a failed check, not evidence), and the definition of 'unsupported.' It also notes the tool replaces a multi-step workflow, providing substantial context 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.

Conciseness5/5

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

The description is detailed yet every sentence carries value: trigger examples, usage context, dual-path behavior, output summary, and an 'IMPORTANT' callout about error semantics. The structure is front-loaded with purpose and examples, followed by a clear warning section, with no redundant or filler content.

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 tool with only two parameters and no output schema, the description thoroughly covers the return payload (verdict types, value with citation, reasoning), the fallback behavior for non-financial claims, and the nuanced error handling for 'could_not_verify.' It gives the agent sufficient context to invoke the tool and interpret results accurately.

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?

The schema already fully documents both parameters with examples (100% coverage). The description adds context about 'exact percent-delta math' for financial claims, which hints at tolerance behavior, but it does not provide additional parameter-level meaning beyond the schema. Thus it meets the baseline for full schema coverage.

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 identifies the tool as a natural-language claim verifier with trigger phrases like 'fact check' and 'verify the claim that...', then details the two verification pipelines and the verdict output. It also highlights that it replaces 4–6 sequential calls, distinguishing it from generic search or research tools.

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 provides a clear usage trigger: 'Use whenever the agent needs to check whether something a user said is factually correct.' It also distinguishes between company-financial and other claims, explaining the different processing paths. However, it does not explicitly mention when not to use the tool or name alternative sibling tools for comparison.

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

Multiple tools have overlapping purposes: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, and deep_research all serve similar question-answering roles. The soil-specific tools are distinct but mixed with many general tools, causing confusion.

Naming Consistency2/5

Naming conventions are inconsistent: some tools use snake_case (ask_pipeworx, list_soil_properties), others are compound nouns (suggest_questions, deep_research), and there is no uniform pattern like verb_noun across the set.

Tool Count3/5

With 34 tools, the count is high but might be justified for a broad platform. However, the server name 'Soilgrids' suggests a focused soil data service, making the count feel excessive and unfocused.

Completeness3/5

For soil data, the three dedicated tools (list_soil_properties, soil_classification, soil_properties) cover basic needs but lack advanced queries. The general tools are extensive but introduce many gaps unrelated to soil, so overall completeness for the server's stated purpose is mediocre.

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