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Glama

Gtex

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

A4.4/5.0
Behavior5/5

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and non-destructive, but the description adds critical behavioral context: the verdict enum, the structured/grounded actual value with citation, and especially the distinction between could_not_verify (verification failed, not evidence) and unsupported (no source found). This is far beyond what annotations convey and directly helps the agent interpret results correctly.

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 relatively lengthy but structurally sound: it opens with invocation cues, states the primary use, explains the two routing paths, enumerates return values, and provides essential caveats. Every sentence carries information, and the length is justified by the tool's complexity. It earns a 4, not a 5, because a few redundant phrases (e.g., listing example phrasings twice) could be trimmed.

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?

This is a complex tool with nuanced return values and failure modes. The description covers the full workflow, defines each verdict, explains error semantics (verification_error{stage,detail}), and even quantifies the efficiency gain ('Replaces 4–6 sequential calls'). Without an output schema, the description carries the burden of explaining return structure, and it does so thoroughly.

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 description coverage is 100% for both parameters (claim and tolerance_pct), so the baseline is 3. The description reinforces the purpose of tolerance_pct by mentioning "exact percent-delta math" and hallucination-detection use, but the schema already explains the default and range. It doesn't add substantial new semantics beyond the schema, so a 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 verb+resource: natural-language claim verification against authoritative sources. It opens with concrete invocation patterns ("Is it true that…", "fact check") and distinguishes itself from siblings by explaining it replaces 4–6 sequential calls. The two distinct paths (SEC EDGAR for company-financial claims vs. grounded pipeline for other facts) further crystallize its scope.

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 says "Use whenever the agent needs to check whether something a user said is factually correct," giving a clear when-to-use directive. It also describes the automatic routing logic, implying that users don't need to decide between paths. However, it doesn't explicitly mention when not to use it or name alternative tools, though the sibling list makes alternatives apparent.

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

ask_pipeworx and ask_pipeworx_beta are currently described as functionally identical, which is a direct ambiguity, and ai_visibility_check/scan_competitor_ai_presence plus the polymarket_* tools create several overlapping boundaries. The long descriptions help, but an agent still has to read deep into each one to avoid selecting the wrong tool.

Naming Consistency3/5

Most tools use readable lowercase snake_case verb_noun names like discover_tools, resolve_entity, and validate_claim, but the set also includes bare nouns like gene and tissues, verb-only memory tools like remember/recall/forget, and noun-phrase names like entity_profile, recent_changes, and pipeworx_trending. The style is not chaotic, but no single convention is sustained.

Tool Count2/5

36 tools exceeds the 25+ threshold and the surface is heavily padded with overlapping meta-tools, near-duplicate routers, and unrelated clusters such as Polymarket arbitrage, AI-visibility checks, and GTEx expression queries. For a server named Gtex, most of these tools are outside the apparent domain, making the count feel overgrown rather than well-scoped.

Completeness2/5

The GTEx-specific subset has only five tools and lacks obvious endpoints like multi-issue eQTLs, isoform expression, or sample-level querying, while the remaining 31 tools belong to unrelated Pipeworx, Polymarket, memory, and subscription domains. There is no coherent single purpose against which the surface can be considered complete, so agents will often hit dead ends or spend calls figuring out what the server is actually for.

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