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Glama

Stat Gl

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?

The description goes well beyond the read-only/idempotent annotations by detailing the two processing paths, the full verdict set, and the critical distinction between could_not_verify and unsupported. It also warns callers not to treat could_not_verify as evidence, which is essential behavioral context not captured by annotations or schema.

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 well-organized, with examples, process paths, return values, and caller warnings. Every sentence adds value, though it could be slightly tightened to improve scannability without losing important nuance.

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 no output schema, the description fully compensates by explaining the return verdicts, the meaning of special cases, and the fallback behavior across claim types. It is comprehensive enough for an agent to know exactly what to expect and how to handle results.

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?

The schema already provides 100% coverage for both parameters, including detailed descriptions of tolerance_pct and claim examples. The tool description adds no additional parameter-level meaning beyond what the schema already contains, so the baseline of 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 purpose: natural-language claim verification against authoritative sources. It gives illustrative query examples and explicitly says it replaces 4–6 sequential calls, distinguishing it from sibling tools like ask_pipeworx or resolve_entity.

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?

The description explicitly says 'Use whenever the agent needs to check whether something a user said is factually correct,' which is a strong when-to-use directive. It also differentiates the SEC EDGAR fast path for company-financial claims from the grounded pipeline for other claims, but it does not mention explicit alternatives or exclusions.

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

A3.8/5.0
Disambiguation2/5

Multiple tools have unclear boundaries: ask_pipeworx_beta currently behaves identically to ask_pipeworx, and the five Polymarket tools (arbitrage, edges, bet_research, fill_risk, edge_tracker) overlap heavily in the 'should I bet on X' use case. The detailed descriptions help, but an agent could easily misselect between these clusters.

Naming Consistency2/5

Naming is highly inconsistent: verb_noun (ask_pipeworx, compare_entities, resolve_entity), noun_noun (entity_profile, table_meta, polymarket_edges), single verbs (remember, forget, recall), and brand-prefixed compounds (pipeworx_trending, polymarket_kalshi_spread) are all mixed together. The server name 'Stat Gl' also doesn't align with the Pipeworx-heavy tool set.

Tool Count2/5

34 tools is well beyond the 25+ threshold that signals an oversized surface, and the set spans disparate domains: data querying, prediction markets, entity research, memory, subscriptions, and even niche utilities like generate_llms_txt and scan_dependency. While each tool has a described purpose, the count feels bloated for a coherent server.

Completeness4/5

Within its actual domains, coverage is strong: query, grounded verification, deep research, claim validation, entity profiles, comparisons, memory CRUD, and subscription lifecycle are all present, plus a complete Statistics Greenland browse/schema/query trio. Minor gaps exist (e.g., limited subscription event types, US-centric company profiles), but agents can typically find a working path without dead ends.

Resources