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Glama

N2yo

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?

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses the full verdict set, the critical distinction between 'could_not_verify' (check did not happen, not evidence) and 'unsupported' (no source covered), and the dual structured/grounded pipeline. This is rich behavioral context that prevents misuse.

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: query examples, use-case, pipeline details, return value, and important caller warnings. Every sentence serves a purpose, though a slight tightening could remove redundancy (e.g., 'authoritative sources' and 'grounded pipeline' are mentioned separately). Still, for the complexity, it is appropriately concise.

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 explains return values (verdict, actual value with citation, reasoning), clarifies two ambiguous verdicts, and describes both execution paths. It also notes the tool replaces multi-step calls, giving a complete picture of what the agent can expect and how to interpret 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 input schema already provides complete descriptions for both parameters (claim and tolerance_pct), including examples and defaults, so schema coverage is 100%. The description adds no additional parameter-level detail beyond the schema, which warrants the baseline score of 3.

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 opens with direct natural-language examples ('Is it true that…', 'fact check', 'verify the claim that…') and defines the resource: natural-language claim verification against authoritative sources. It clearly distinguishes from sibling tools by focusing on checking the factual correctness of user claims and even mentions it replaces multiple sequential calls.

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.' It also details two execution paths (SEC EDGAR for company-financial claims, grounded pipeline for all others), which helps the agent anticipate behavior. However, it does not name alternative tools or state explicit when-not-to-use conditions, so it falls short of a 5.

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 heavily overlapping purposes: ask_pipeworx, ask_pipeworx_beta, and ask_pipeworx_grounded are near-duplicates (beta is currently identical), and the five Polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) require careful reading to distinguish. Additionally, entity_profile, compare_entities, and recent_changes all handle company data, and ai_visibility_check vs scan_competitor_ai_presence are clearly paired. The detailed descriptions help, but an agent will frequently misselect among these clusters.

Naming Consistency3/5

All names are lowercase with underscores, so the style is internally consistent. However, the pattern is mixed: many use verb_noun (get_positions, list_subscriptions, resolve_entity) but several are noun-first or noun-only (entity_profile, polymarket_edges, pipeworx_trending, bet_research). There's no strong verb/noun convention across the set, making the naming pattern less predictable than it could be.

Tool Count2/5

With 34 tools, the set exceeds the 16–25 'heavy' range and sits in the 'too many' band. The server name suggests a focused satellite-tracking service, yet only 3 tools (get_positions, get_visual_passes, whats_above) serve that purpose; the other 31 cover unrelated domains like data research, prediction markets, memory, and subscriptions. Even as a general-purpose research platform, the count feels bloated and unfocused.

Completeness3/5

The tool surface is broad, covering satellite tracking, data research, prediction markets, subscriptions, and memory, and within each cluster the main operations exist (e.g., subscription lifecycle, edge analysis + fill risk). However, the scattered scope creates gaps: there's no direct raw-data fetch tool (everything goes through ask_pipeworx), no general web search, and the presence of unrelated utilities (generate_llms_txt, scan_dependency) suggests the domain boundaries are unclear. For its stated satellite purpose, the satellite tools are thin (no TLE, no catalog, no detailed orbit info).

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