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Glama

Irail

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. 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 (readOnlyHint=true, openWorldHint=true, idempotentHint=true), the description discloses critical behavioral nuances: the meaning of could_not_verify (does not happen, must not be treated as evidence) and unsupported (no source found). This is valuable context that prevents misinterpretation of results and goes well beyond the annotation set.

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 longer than average but every sentence serves a purpose: usage triggers, routing, return values, and error semantics. It is densely packed and front-loaded with examples, though a bit lengthy. It earns a 4 for structure and relevance, not a 5 for brevity.

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 components (verdict list, actual value with citation, reasoning) and both error states (could_not_verify and unsupported). It also notes the tool consolidates 4–6 sequential calls, giving a complete picture for an agent. This is fully adequate for the tool's complexity.

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% parameter coverage, with detailed descriptions for both claim (with examples) and tolerance_pct (including range, override semantics, and default). The tool description adds no new parameter-specific information, 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 opens with concrete natural-language triggers ("Is it true that…", "fact check") and then states the tool's function explicitly: "natural-language claim verification against authoritative sources." It clearly identifies the specific verb (verify) and resource (claims), and distinguishes itself from general Q&A siblings by focusing on factual correctness checks.

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 gives explicit usage guidance: "Use whenever the agent needs to check whether something a user said is factually correct." It also explains routing behavior for company-financial vs. other claims. However, it does not name specific alternative tools or provide explicit when-not-to-use scenarios, 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

A4/5.0
Disambiguation4/5

Most tools have distinct purposes with detailed descriptions. However, the three ask_pipeworx variants (standard, beta, grounded) and the five polymarket tools (arbitrage, edges, edge_tracker, fill_risk, kalshi_spread) have overlapping functionality that could confuse an agent if descriptions are not carefully parsed. The memory tools (remember, recall, forget) are clear. Overall, the set is mostly disambiguated.

Naming Consistency3/5

Tool names use snake_case consistently, but naming patterns vary: some follow verb_noun (e.g., ai_visibility_check, compare_entities), others are noun_noun (e.g., bet_research, entity_profile), and a few are single verbs (e.g., recall, remember, forget, subscribe). This mixed pattern reduces predictability, though the names are still readable.

Tool Count2/5

At 35 tools, the server is overly broad, covering Belgian rail, Pipeworx data queries, prediction markets, memory, subscriptions, and more. The server name 'Irail' suggests a focused rail toolset, but rail is only a small part. This scope mismatch and high tool count make it feel bloated and less coherent.

Completeness4/5

Within each subdomain, the tool surface is fairly complete. Belgian rail has journey planning, liveboard, train tracking, and disturbances. Data querying has universal, grounded, deep research, entity profiles, comparisons, recent changes, and claim validation. Prediction markets have arbitrage, edge detection, edge tracking, fill risk, and cross-venue spread. Memory and subscriptions are covered. Minor gaps exist (e.g., no tool for deleting entities or canceling orders), but overall coverage is strong.

Resources