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Glama

Thegamesdb

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?

Even though annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint false, the description adds significant behavioral context: the routing logic, the return verdict vocabulary, the meaning of could_not_verify vs unsupported, citation format, and that it replaces multiple calls. No contradiction with annotations.

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 long but every part earns its place: trigger phrases, routing rules, return details, and critical caller warnings. It's front-loaded with natural-language query examples, and the 'IMPORTANT for callers' section is clearly highlighted. No filler or redundancy.

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?

Given the tool has only 2 parameters, no output schema, and moderate complexity, the description is remarkably complete: it explains input style, internal routing, output verdicts, evidence citation, error semantics, and alternatives. It fully prepares an agent to invoke and interpret results correctly.

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%, so the baseline is 3. The description adds extra meaning beyond the schema by explaining how tolerance_pct interacts with 'the tolerance implied by the claim wording' and by giving concrete claim examples for the claim parameter. This enriches parameter understanding without replacing 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 starts with a clear verb—validate—and resource (claim), then gives natural-language trigger examples and explicitly contrasts with siblings like ask_pipeworx_grounded and deep_research by noting it replaces 4–6 sequential calls. It clearly distinguishes the tool's purpose from other list/entity/search tools.

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?

States 'Use whenever the agent needs to check whether something a user said is factually correct.' It also gives explicit when-to-use paths: company-financial claims go through SEC EDGAR, any other factual claim routes to the grounded pipeline. It provides strong exclusions and warnings, like 'could_not_verify means the check did not happen... must not be shown as one.'

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

A3.6/5.0
Disambiguation2/5

ask_pipeworx and ask_pipeworx_beta are explicitly described as functionally identical right now, creating direct ambiguity. The six polymarket tools (edges, arbitrage, edge_tracker, fill_risk, kalshi_spread, bet_research) have blurred boundaries for prediction-market tasks, and ai_visibility_check vs scan_competitor_ai_presence is a wrapper relationship. Only the memory trio and subscription lifecycle are cleanly distinct.

Naming Consistency2/5

Naming follows multiple conventions with no global pattern: bare verbs (remember, recall, forget, subscribe), product-prefixed verbs (ask_pipeworx, pipeworx_feedback), noun phrases (entity_profile, deep_research, recent_changes), and verb_noun snake_case (discover_tools, validate_claim). There are consistent pockets (the polymarket_* family, the TheGamesDB get_/list_/search_ verbs), but the overall mix across 35 tools is inconsistent.

Tool Count2/5

35 tools exceeds the 25-tool threshold for a heavy server, and the count is wildly disproportionate to the server's stated identity: only 4 of 35 tools relate to TheGamesDB while 31 belong to an unrelated Pipeworx data/prediction-market suite. The game database would justify roughly 5-10 tools, so the bulk of this surface is out of scope for the server name.

Completeness3/5

The Pipeworx portion is genuinely thorough — discovery, grounded querying, entity resolution, claim validation, subscription lifecycle, memory, and feedback form a coherent coverage. However, the namesake TheGamesDB surface is thin: search, get-by-id, list genres, and list platforms, with no games-by-platform/genre browsing, no media/screenshots beyond front boxart, and no updates feed. The set as a whole covers multiple unrelated domains with no single complete lifecycle.

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