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Glama

Mojang

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

A4.6/5.0
Behavior5/5

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

Annotations already indicate readOnly/idempotent/non-destructive behavior. The description adds substantial behavioral detail: the two distinct processing paths (SEC EDGAR vs grounded), the meaning of could_not_verify as a non-result rather than evidence, the distinction between could_not_verify and unsupported, and the citation format. It also warns callers not to present could_not_verify as evidence, which goes beyond schema/annotations.

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 (~180 words) but each sentence carries unique value: trigger phrases, usage, pipeline details, return values, and a critical caller warning. It is front-loaded with purpose and structured logically, though slightly verbose for the complexity involved.

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?

Despite no output schema, the description fully explains the return structure (verdict types, actual value with citation, reasoning) and error semantics (could_not_verify with verification_error, unsupported meaning). This covers edge cases and gives a complete picture for a complex tool, making it highly usable without additional documentation.

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 coverage is 100% for both parameters. The description adds extra meaning by providing concrete examples of claim statements and explaining tolerance_pct semantics: it overrides the implied tolerance, has a range/cap, and suggests 1-2 for hallucination detection. This enhances understanding beyond the schema descriptions.

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 defines the tool as natural-language claim verification against authoritative sources, listing trigger phrases like "fact check" and "verify the claim that." It distinguishes itself from sibling tools by specifying the verdict-based output and the replacement of 4-6 sequential calls.

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?

It explicitly states when to use it: "Use whenever the agent needs to check whether something a user said is factually correct." It also differentiates between company-financial claims (SEC EDGAR path) and other claims (grounded pipeline). However, it does not name specific alternatives or explicitly state when not to use it, so it lacks full exclusion guidance.

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

ask_pipeworx and ask_pipeworx_beta are currently described as functionally identical, creating a clear misselection risk, and several query/answer tools (ask_pipeworx, ask_pipeworx_grounded, deep_research, validate_claim, entity_profile, compare_entities) have overlapping boundaries. The Polymarket tools also blur into each other, so despite verbose descriptions, an agent can easily route a request to the wrong tool.

Naming Consistency3/5

All names are snake_case and readable, but the conventions are mixed: verb_noun (ask_pipeworx, resolve_entity), noun_noun (entity_profile, bet_research), bare verbs (remember, recall, forget, profile), and prefix families with inconsistent ordering (ask_pipeworx vs pipeworx_feedback, polymarket_edges vs polymarket_kalshi_spread). This is not chaotic, but there is no single predictable naming pattern.

Tool Count2/5

36 tools is well past the 25+ threshold for a heavy surface, and the server name 'Mojang' implies a narrow Minecraft API scope while only 5 tools relate to Minecraft. The rest belong to a broad data-research, prediction-market, and memory platform, making the tool count feel inflated and mis-scoped for the server's stated identity.

Completeness2/5

For the implied Minecraft/Mojang domain, there are obvious gaps such as no authentication, skin/name mutation, or broader account endpoints, so that surface is thin. Meanwhile, the Pipeworx data side is fairly complete, but because two unrelated domains are jammed into one server, neither domain is covered in a coherent, trustworthy way.

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