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Glama

Giantbomb

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

A4.9/5.0
Behavior5/5

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

Annotations declare read-only, open-world, and idempotent traits, but the description goes far beyond by explaining exact verdict types, the meaning of could_not_verify vs unsupported, the verification_error field, and the routing logic. This is substantial behavioral context not available in annotations alone.

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 information-dense, with each sentence contributing valuable details (triggers, routing, verdicts, error semantics, efficiency). It's well-organized and front-loaded with purpose and usage, though some redundancy could be trimmed without losing value.

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 complex tool with multiple pipelines, verdict types, and error handling, the description is remarkably complete. It explains return values in the absence of an output schema, covers edge cases (could_not_verify, unsupported), and provides sufficient context for safe invocation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% with descriptions for both claim and tolerance_pct. The description adds extra meaning for tolerance_pct: overrides implied tolerance, recommended 1-2 for hallucination detection, and default cap of 5. It also gives concrete examples for the claim parameter, enhancing clarity.

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: natural-language claim verification against authoritative sources. It provides multiple trigger phrases and distinguishes itself from siblings by noting it replaces 4-6 sequential calls, making it the dedicated fact-checking tool rather than a general search or research tool.

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

Usage Guidelines5/5

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

Explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct.' It also differentiates between company-financial claims (SEC EDGAR fast path) and any other claim (grounded pipeline), and warns that could_not_verify should not be treated as evidence, providing clear when-not-to-use semantics.

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

C2.8/5.0
Disambiguation2/5

Several tool families have blurry boundaries: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all route into the same underlying catalog with only subtle differences, and the six polymarket_* tools overlap heavily (edge scanning vs. fill-risk vs. arbitrage vs. cross-venue spread). Compounding this, the server is named 'Giantbomb' but only 7 of 38 tools relate to that domain, so an agent cannot predict what this server does from its name.

Naming Consistency3/5

All tool names are snake_case, which helps, but the verb pattern is inconsistent: verb_noun (list_subscriptions, resolve_entity, validate_claim), bare verbs (remember, recall, forget), noun-prefix subjects (entity_profile, pipeworx_feedback, polymarket_edges, recent_changes), and even a versioned name (ask_pipeworx_beta) that breaks its own family's pattern. There is no predictable naming scheme an agent could use to guess a tool's name.

Tool Count2/5

38 tools is well past the 25+ 'too many' threshold, and the count is split across two unrelated concerns: ~7 Giant Bomb tools (several explicitly marked 'API offline as of 2026-05') and ~31 tools for a Pipeworx data-research and Polymarket-trading platform. The number is not merely heavy; it serves no single coherent purpose.

Completeness2/5

For the declared Giant Bomb domain, the surface is incomplete (missing videos, franchises, reviews, people, and other known resource types) and largely dead since the underlying API is offline. The Pipeworx half is comparatively complete with routing, grounded answers, research, memory, subscriptions, and feedback, but that coverage is attached to the wrong server identity and does not fill the gaps in the claimed purpose.

Resources