Skip to main content
Glama

Tatoeba

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?

Beyond the annotations (readOnly, openWorld, idempotent), the description adds substantial behavioral context: it details the output verdict options, explains the meaning of 'could_not_verify' and 'unsupported' and how they must be interpreted, and describes the internal routing logic. This far exceeds what annotations alone convey and is crucial for correct agent behavior.

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 conveys a distinct piece of information: examples, use cases, routing, return values, error semantics, and efficiency gains. It is structured with a clear intro and detailed follow-on, and while slightly dense, it is well-organized and front-loaded with the core purpose.

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's complexity (two execution paths, multiple verdict types, error states), the description is comprehensive. It covers what the tool does, how it routes, what it returns, and how to interpret edge-case verdicts. The absence of an output schema is compensated by the explicit description of return values, making the description complete for agent usage.

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?

Schema coverage is 100%, so the baseline is 3. The description adds extra meaning for 'tolerance_pct' by explaining it overrides claim-wording tolerance and gives a specific use case (hallucination detection, set 1–2). It also clarifies the cap of 5, which is not in the schema, thus adding genuine semantic value.

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 as natural-language claim verification against authoritative sources, with explicit verb (verify) and resource (claims). It distinguishes itself from siblings by positioning it as the fact-checking tool and even mentions it replaces 4–6 sequential calls, setting it apart from generic search/research tools.

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,' giving clear when-to-use guidance. It also explains the two routing paths (SEC EDGAR for company-financial, grounded pipeline for other claims), but does not name alternative tools or state when not to use, so it misses the 'when-not' element for a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All Tatoeba toolsInspect
Try in Browser
RayrunBuild, deploy, and operate MCP servers without becoming an infrastructure team.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

B3/5.0
Disambiguation2/5

Many tools have overlapping purposes (e.g., ask_pipeworx, ask_pipeworx_grounded, deep_research; multiple Polymarket tools). The memory tools (remember/recall/forget) are generic and could be confused with each other. Overall, there is significant ambiguity in tool selection.

Naming Consistency3/5

Most tool names use snake_case, but there is no strong verb_noun pattern. Some are simple nouns (languages, search) while others are verbs (forget, recall). The naming is readable but not highly consistent.

Tool Count1/5

With 34 tools, the set is far too large for a Tatoeba server. Only 4 tools (search, sentence, translations, languages) are actually related to Tatoeba; the rest cover unrelated domains (Pipeworx data, Polymarket, AI visibility). This is an extreme mismatch.

Completeness2/5

For the Tatoeba domain, the 4 tools cover basic functionality but lack operations like adding or editing sentences. The overwhelming presence of irrelevant tools makes the surface feel incomplete and disjointed for the server's stated purpose.

Resources