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Glama

Gnomad

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

A4.6/5.0
Behavior5/5

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

Annotations already flag readOnly/idempotent/non-destructive, and the description goes further by disclosing the return verdict set and critically explaining that 'could_not_verify' means the check did not happen and is NOT evidence for or against the claim, while 'unsupported' means no source exists. This prevents misinterpretation and is not contradicted by the 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 long but dense, with each segment adding value: trigger phrases, routing, return values, error semantics, and efficiency. The list of natural-language phrasings could be trimmed, but it is front-loaded with the use case and the critical caveats are included.

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 the return payload: six verdict values, actual value with citation, reasoning, and error object. It also covers both routing paths and the critical distinction between 'could_not_verify' and 'unsupported', making the tool self-contained.

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 documents both parameters with 100% coverage. The description adds practical guidance beyond the schema: a real-world claim example and explicit tolerance advice ('set 1–2 for hallucination detection') plus the default cap of 5, which helps the agent use 'tolerance_pct' correctly.

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?

Description clearly identifies the tool as natural-language claim verification ('fact check', 'verify the claim that…') and specifies scope: fact-checking user statements against authoritative sources. It also distinguishes from siblings by explicitly separating the structured SEC EDGAR fast path for company-financial claims from the grounded pipeline for all other factual claims.

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?

Explicitly states 'Use whenever the agent needs to check whether something a user said is factually correct' and provides routing guidance: company-financial claims use SEC EDGAR, any other claim falls through to the grounded pipeline. It does not name exclusions or alternative tools, but the routing and efficiency note ('Replaces 4–6 sequential calls') give clear when-to-use context.

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

B3.4/5.0
Disambiguation2/5

The set mixes several overlapping query surfaces: ask_pipeworx and ask_pipeworx_beta are currently identical, ask_pipeworx_grounded/deep_research/discover_tools/suggest_questions all serve related retrieval/discovery purposes, and the five polymarket_* tools have similar opportunity-finding goals. Only the unusually detailed descriptions save some tools from misselection; an agent would struggle to quickly pick the right one.

Naming Consistency4/5

Names are uniformly snake_case and mostly follow a verb_noun or prefixed_noun pattern (ask_pipeworx, validate_claim, polymarket_edges, scan_dependency). Minor inconsistencies exist — bare nouns like gene/variant/search sit alongside compound names like generate_llms_txt, and the pipeworx_ prefix isn't applied to ask_pipeworx/deep_research — but the overall style is recognizable and predictable.

Tool Count2/5

36 tools is too many for a coherent server, especially since the domains are largely unrelated: 5 gnomAD genomics tools, 20+ Pipeworx/Polymarket data tools, memory CRUD, subscription management, and a couple of web-dev utilities. The count doesn't align with a single obvious scope and would overwhelm an agent selecting among them.

Completeness3/5

Within the major subdomains coverage is strong: memory has remember/recall/forget, subscriptions have full lifecycle tools, and Polymarket has edge detection plus fill-risk checking. However, there are notable gaps — no tool to fetch a pipeworx:// citation URI despite deep_research promising resolvable citations, and the gnomAD surface lacks batch queries, coverage, or constraint data for a server named Gnomad.

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