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Glama

Rawg

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

A4.7/5.0
Behavior5/5

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

The description goes well beyond the annotations by explaining the routing logic (SEC EDGAR path vs. grounded pipeline), the meaning of each verdict (including the crucial distinction between 'could_not_verify' meaning the check did not happen and 'unsupported' meaning no source exists), and the presence of verification_error{stage,detail}. This adds significant operational context that annotations do not cover.

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?

Although the description is long, every sentence carries critical information: examples, routing, return values, verdict semantics, and error handling. It is front-loaded with the purpose and remains tightly organized with no redundant phrases. The length is 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?

The description fully explains the tool's behavior without an output schema: it covers what it takes as input, how it processes different claim types, what it returns (verdicts with specific meanings), and how to interpret special cases. It also notes the tool's efficiency over sequential calls, making it self-contained enough for an agent to use 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?

The input schema already provides 100% coverage with detailed descriptions for both parameters, including tolerance_pct range and default behavior. The description reinforces this by mentioning 'exact percent-delta math' and the tolerance override concept, but adds minimal new information beyond the schema. The baseline of 3 is exceeded due to the schema's exceptional detail and the description's integration of parameter meaning into the overall behavior.

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 verification tool against authoritative sources, with concrete example phrasings. It distinguishes two modes (company-financial vs. any other claim) and explicitly states the output (verdict + value + citation), making the purpose highly specific and distinguishable from sibling 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?

It explicitly states when to use the tool: 'whenever the agent needs to check whether something a user said is factually correct.' It also covers the two routing scenarios and notes it replaces 4–6 sequential calls, but does not name specific alternatives or exclusions. Clear context without explicit sibling differentiation.

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

Multiple tools have unclear boundaries: ask_pipeworx_beta is explicitly identical to ask_pipeworx, bet_research overlaps heavily with polymarket_edges, and entity_profile/recent_changes/compare_entities/validate_claim all pull from the same SEC/news fundamentals space. The memory trio and game lookup tools are distinct, but too many other tools could be confused for one another.

Naming Consistency3/5

The surface is uniformly snake_case and has some strong families (list_*, polymarket_*, ask_pipeworx_*), but it mixes verb-led names (search_games, remember, validate_claim) with noun-led names (entity_profile, polymarket_arbitrage, pipeworx_trending) and brand-style names like ask_pipeworx. There is a pattern, but it is not a single consistent one.

Tool Count2/5

35 tools is a heavy surface, and the set feels sprawling rather than focused: a four-tool RAWG game submodule sits alongside a general-purpose research platform, Polymarket edge tooling, memory helpers, subscriptions, feedback, and meta-utilities. Many of these could have been consolidated or split into separate servers.

Completeness3/5

As a general data-research and prediction-market platform the coverage is strong, with lookup, grounded verification, entity resolution, research fan-out, and subscription flows. But for a server named Rawg, the game domain is thinly covered with only search/get/list tools and no game-detail enrichment or broader browsing surface, leaving the core domain feeling like an afterthought.

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