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Glama

crossref

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. Added

TDQS

A4.5/5.0
Behavior5/5

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

The description discloses detailed behavioral traits beyond the annotations: the SEC EDGAR + XBRL fast path, the grounded pipeline fallback, the full verdict set, pipeworx:// citations, and the crucial distinction between could_not_verify (verification failed) and unsupported (no source). This adds significant context beyond the read-only/idempotent annotations.

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?

The description is dense but logically organized: trigger phrases, usage, routing behavior, return values, and critical caveats. Every sentence adds value without redundancy, making it appropriately sized for 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?

Given no output schema, the description fully covers return values (verdict types, citation, reasoning) and explains error behavior (could_not_verify vs unsupported). It also mentions that it replaces multiple sequential calls, providing full context for agent decision-making.

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 coverage is 100% with both parameters thoroughly described in the input schema. The description adds a small amount of behavioral context (e.g., exact percent-delta math, tolerance cap), but the schema already carries the semantic load, so this is at the acceptable baseline.

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 verifies natural-language factual claims against authoritative sources, with specific trigger phrases and a clear verb+resource ('verify the claim that…'). It distinguishes itself as a consolidated alternative to multi-step lookups, making the purpose unambiguous.

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' and explains the two routing paths for company-financial vs. other claims. However, it does not name specific sibling tools or state explicit exclusion criteria, so it misses the highest bar for alternative guidance.

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

Several tools overlap heavily: ask_pipeworx, ask_pipeworx_beta, ask_pipeworx_grounded, deep_research, discover_tools, and suggest_questions all serve queries, with ask_pipeworx_beta explicitly identical to ask_pipeworx. Prediction-market tools (polymarket_edges, polymarket_arbitrage, polymarket_fill_risk, bet_research, etc.) also have unclear boundaries, making tool selection tricky for an agent.

Naming Consistency4/5

Tool names are consistently lowercase snake_case with a mostly verb-first pattern (get_work, search_works, list_subscriptions, validate_claim). Minor deviations exist: some names are noun-first (entity_profile, recent_changes) and prefixes vary (get/search/list/ask/scan), but the overall convention is predictable and readable.

Tool Count2/5

34 tools is far too many for a server named 'crossref', and only three tools actually relate to Crossref. The rest form a sprawling utility belt covering data routing, memory, subscriptions, prediction markets, AI visibility, and npm scanning — a scope mismatch that makes the server feel like a kitchen sink rather than a focused offering.

Completeness2/5

The Crossref-specific surface is thin: search_works, get_work, and get_journal cover discovery and metadata but lack citation lookup, author search, and funder information. The broader Pipeworx surface is extensive but has no unifying domain, so it's impossible to consider the overall toolset complete for any coherent purpose.

Resources