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Glama

Mhw

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. Added

TDQS

A4.4/5.0
Behavior5/5

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

Beyond annotations (readOnly, openWorld, idempotent), the description reveals two routing paths (SEC EDGAR/XBRL vs grounded pipeline), the full verdict set, and the critical semantics of could_not_verify (not evidence) and unsupported (no source found). It also discloses that could_not_verify carries a structured verification_error. This is rich behavioral context with no contradiction to 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 dense but well-organized: example queries, routing, verdict list, and an IMPORTANT caller warning. Each sentence adds value, though the opening example list is somewhat long. Not as tight as a two-sentence high-efficiency description, but justified by 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 fully specifies expected return values: the verdict enum, the grounded/structured value with citation, reasoning, and verification_error details. It also covers edge cases (could_not_verify vs unsupported) and usage context, making the tool usage complete for an agent.

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 parameter descriptions are comprehensive (100% coverage) for claim and tolerance_pct, including defaults and override guidance. The tool description adds minimal param-specific info beyond reinforcing that claim is natural language and referencing exact percent-delta math. Baseline 3 is appropriate.

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: it validates natural-language factual claims, with explicit example queries. It distinguishes itself from siblings by noting it replaces a 4–6 step pipeline and handles both structured SEC EDGAR and grounded fallback paths.

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 implies alternatives by explaining it replaces sequential calls, but does not name specific sibling tools or explicitly state when not to use it.

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

B3.3/5.0
Disambiguation2/5

Several tool groups heavily overlap: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, and discover_tools all route to the same 5,798-tool catalog and compete for the same 'answer this question' use case. The Polymarket tools (bet_research, polymarket_edges, polymarket_arbitrage, polymarket_edge_tracker) also all detect betting opportunities, making it easy to pick the wrong one.

Naming Consistency2/5

Naming conventions are mixed: verb_noun (ask_pipeworx, compare_entities, validate_claim), noun_noun (entity_profile, polymarket_arbitrage), adjective_noun (recent_changes, deep_research), and get_* for the MHW tools. All names use snake_case, but there is no consistent structural pattern across the set.

Tool Count2/5

35 tools is too many for a coherent server, and the bulk of them (31 tools) are unrelated to the server's apparent 'Mhw' identity, which covers only 4 Monster Hunter World tools. The set reads like three separate servers (MHW game data, Pipeworx research, Polymarket betting) merged into one.

Completeness1/5

For a server named 'Mhw', the MHW surface is severely incomplete: armor, monsters, skills, and weapons exist, but quests, items, decorations, crafting, and locations are missing. The non-MHW tools are broad but belong to a different domain, so the server does not come close to covering its apparent intended purpose.

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